From 8b64502b0db8deb847edc98b4313438efca7e1c1 Mon Sep 17 00:00:00 2001 From: Aditya Chivukula Date: Sun, 11 Jan 2026 23:05:03 +0100 Subject: [PATCH 1/8] add mcp server for documentation because why not --- .cursor/mcp.json | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 .cursor/mcp.json diff --git a/.cursor/mcp.json b/.cursor/mcp.json new file mode 100644 index 00000000..e256850d --- /dev/null +++ b/.cursor/mcp.json @@ -0,0 +1,8 @@ +{ + "mcpServers": { + "catanatron": { + "type": "sse", + "url": "https://docs.catanatron.com/~gitbook/mcp" + } + } +} \ No newline at end of file From 383ce75ae19b9feefe5eeeacdc13541efa57b135 Mon Sep 17 00:00:00 2001 From: Aditya Chivukula Date: Mon, 12 Jan 2026 06:57:21 +0100 Subject: [PATCH 2/8] include llm player --- catanatron/catanatron/cli/cli_players.py | 80 + catanatron/catanatron/cli/play.py | 88 +- catanatron/catanatron/players/__init__.py | 66 + catanatron/catanatron/players/llm/__init__.py | 20 + catanatron/catanatron/players/llm/base.py | 287 ++ catanatron/catanatron/players/llm/history.py | 102 + .../catanatron/players/llm/output_types.py | 239 + .../catanatron/players/llm/state_formatter.py | 394 ++ catanatron/catanatron/players/llm/tools.py | 539 +++ catanatron/catanatron/players/llm_player.py | 221 + pyproject.toml | 14 +- tests/test_llm_player.py | 447 ++ uv.lock | 4239 +++++++++++++++++ 13 files changed, 6714 insertions(+), 22 deletions(-) create mode 100644 catanatron/catanatron/players/llm/__init__.py create mode 100644 catanatron/catanatron/players/llm/base.py create mode 100644 catanatron/catanatron/players/llm/history.py create mode 100644 catanatron/catanatron/players/llm/output_types.py create mode 100644 catanatron/catanatron/players/llm/state_formatter.py create mode 100644 catanatron/catanatron/players/llm/tools.py create mode 100644 catanatron/catanatron/players/llm_player.py create mode 100644 tests/test_llm_player.py create mode 100644 uv.lock diff --git a/catanatron/catanatron/cli/cli_players.py b/catanatron/catanatron/cli/cli_players.py index 0fef2b35..e33ef1bd 100644 --- a/catanatron/catanatron/cli/cli_players.py +++ b/catanatron/catanatron/cli/cli_players.py @@ -1,3 +1,4 @@ +import os from collections import namedtuple from rich.table import Table @@ -13,6 +14,56 @@ # Player must have a CODE, NAME, DESCRIPTION, CLASS. CliPlayer = namedtuple("CliPlayer", ["code", "name", "description", "import_fn"]) + + +# ============= LLM Player Factory Functions ============= +# These are lazy-loaded to avoid import errors when pydantic-ai is not installed + + +def _get_default_model(): + """Get default LLM model from environment or use Claude.""" + return os.environ.get("CATAN_LLM_MODEL", "anthropic:claude-sonnet-4-20250514") + + +def create_llm_player(color, model=None): + """Factory for pure LLM player.""" + from catanatron.players.llm_player import PydanticAIPlayer + + model = model or _get_default_model() + return PydanticAIPlayer(color, model=model) + + +def create_llm_alphabeta_player(color, model=None, depth="2", prunning="False"): + """Factory for LLM + AlphaBeta hybrid player.""" + from catanatron.players.llm_player import LLMAlphaBetaPlayer + + model = model or _get_default_model() + return LLMAlphaBetaPlayer( + color, + model=model, + depth=int(depth), + prunning=prunning.lower() == "true", + ) + + +def create_llm_mcts_player(color, model=None, num_simulations="10"): + """Factory for LLM + MCTS hybrid player.""" + from catanatron.players.llm_player import LLMMCTSPlayer + + model = model or _get_default_model() + return LLMMCTSPlayer( + color, + model=model, + num_simulations=int(num_simulations), + ) + + +def create_llm_value_player(color, model=None): + """Factory for LLM + Value Function hybrid player.""" + from catanatron.players.llm_player import LLMValuePlayer + + model = model or _get_default_model() + return LLMValuePlayer(color, model=model) CLI_PLAYERS = [ CliPlayer( "H", "HumanPlayer", "Human player, uses input() to get action.", HumanPlayer @@ -64,6 +115,35 @@ "AlphaBeta but searches only within turn", SameTurnAlphaBetaPlayer, ), + # LLM Players (require pydantic-ai, anthropic, or openai packages) + CliPlayer( + "LLM", + "PydanticAIPlayer", + "Pure LLM player using PydanticAI. Set ANTHROPIC_API_KEY or OPENAI_API_KEY env var. " + "Optional param: MODEL (e.g., LLM:openai:gpt-4o)", + create_llm_player, + ), + CliPlayer( + "LLMAB", + "LLMAlphaBetaPlayer", + "LLM with AlphaBeta strategy advisor. Params: MODEL, DEPTH, PRUNNING. " + "Example: LLMAB:anthropic:claude-sonnet-4-20250514:3:True or LLMAB::3 to use default model", + create_llm_alphabeta_player, + ), + CliPlayer( + "LLMM", + "LLMMCTSPlayer", + "LLM with MCTS strategy advisor. Params: MODEL, NUM_SIMULATIONS. " + "Example: LLMM:openai:gpt-4o:20 or LLMM::20 to use default model", + create_llm_mcts_player, + ), + CliPlayer( + "LLMV", + "LLMValuePlayer", + "LLM with Value Function strategy advisor. Params: MODEL. " + "Example: LLMV:anthropic:claude-sonnet-4-20250514 or just LLMV for default", + create_llm_value_player, + ), ] diff --git a/catanatron/catanatron/cli/play.py b/catanatron/catanatron/cli/play.py index cf098f95..0d4fa4d7 100644 --- a/catanatron/catanatron/cli/play.py +++ b/catanatron/catanatron/cli/play.py @@ -6,10 +6,8 @@ import click from rich.console import Console from rich.table import Table -from rich.progress import Progress from rich.progress import Progress, BarColumn, TimeRemainingColumn from rich import box -from rich.console import Console from rich.theme import Theme from rich.text import Text @@ -18,6 +16,9 @@ from catanatron.models.map import build_map from catanatron.state_functions import get_actual_victory_points +# Optional Logfire integration for orchestration logging +import logfire + # try to suppress TF output before any potentially tf-importing modules os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" from catanatron.utils import ensure_dir, format_secs @@ -166,6 +167,13 @@ def simulate( catanatron-play --players VP,F --num 10 --output data/ --ouput-format json\n catanatron-play --players W,F,AB:3 --num 1 --ouput-format csv --db --quiet """ + # Configure Logfire - send to service but don't interfere with console output + logfire.configure( + send_to_logfire=True, + console=False, # Don't print logs to console to avoid interfering with Rich + ) + logfire.instrument_pydantic_ai() + if code: abspath = os.path.abspath(code) spec = importlib.util.spec_from_file_location("module.name", abspath) @@ -183,13 +191,22 @@ def simulate( output, output_format, include_board_tensor, db, step_db ) game_config = GameConfigOptions(config_discard_limit, config_vps_to_win, config_map) - play_batch( - num, - players, - output_options, - game_config, - quiet, - ) + + # Wrap play_batch with Logfire span if available + with logfire.span( + "catanatron.play_batch", + num_games=num, + num_players=len(players), + output_format=output_format, + map_type=config_map, + ): + play_batch( + num, + players, + output_options, + game_config, + quiet, + ) @dataclass(frozen=True) @@ -235,18 +252,25 @@ def play_batch_core(num_games, players, game_config, accumulators=[]): if isinstance(accumulator, SimulationAccumulator): accumulator.before_all() - for _ in range(num_games): - for player in players: - player.reset_state() - catan_map = build_map(game_config.catan_map) - game = Game( - players, - discard_limit=game_config.discard_limit, - vps_to_win=game_config.vps_to_win, - catan_map=catan_map, - ) - game.play(accumulators) - yield game + for game_num in range(num_games): + # Wrap each game with Logfire span if available + with logfire.span( + "catanatron.play_game", + game_number=game_num + 1, + total_games=num_games, + map_type=game_config.catan_map, + ): + for player in players: + player.reset_state() + catan_map = build_map(game_config.catan_map) + game = Game( + players, + discard_limit=game_config.discard_limit, + vps_to_win=game_config.vps_to_win, + catan_map=catan_map, + ) + game.play(accumulators) + yield game for accumulator in accumulators: if isinstance(accumulator, SimulationAccumulator): @@ -263,6 +287,17 @@ def play_batch( output_options = output_options or OutputOptions() game_config = game_config or GameConfigOptions() + # Log player types if Logfire is available + # if LOGFIRE_AVAILABLE: + # player_types = [type(p).__name__ for p in players] + # logfire.info( + # "Starting batch simulation", + # num_games=num_games, + # players=player_types, + # output_format=output_options.output_format, + # map_type=game_config.catan_map, + # ) + statistics_accumulator = StatisticsAccumulator() vp_accumulator = VpDistributionAccumulator() accumulators = [statistics_accumulator, vp_accumulator] @@ -444,6 +479,17 @@ def play_batch( f"{output_options.output_format} files saved at: [green]{output_options.output}[/green]" ) + # Log summary to Logfire if available + # if LOGFIRE_AVAILABLE: + # logfire.info( + # "Batch simulation completed", + # num_games=num_games, + # avg_ticks=statistics_accumulator.get_avg_ticks(), + # avg_turns=statistics_accumulator.get_avg_turns(), + # avg_duration=statistics_accumulator.get_avg_duration(), + # wins=dict(statistics_accumulator.wins), + # ) + return ( dict(statistics_accumulator.wins), dict(statistics_accumulator.results_by_player), diff --git a/catanatron/catanatron/players/__init__.py b/catanatron/catanatron/players/__init__.py index e69de29b..3d5cd790 100644 --- a/catanatron/catanatron/players/__init__.py +++ b/catanatron/catanatron/players/__init__.py @@ -0,0 +1,66 @@ +""" +Catanatron player implementations. + +This package provides various AI player implementations for playing Catan, +from simple random players to sophisticated LLM-powered agents. +""" + +# Base players (from models.player) +from catanatron.models.player import ( + Player, + SimplePlayer, + RandomPlayer, + HumanPlayer, + Color, +) + +# Strategy players +from catanatron.players.weighted_random import WeightedRandomPlayer +from catanatron.players.search import VictoryPointPlayer +from catanatron.players.value import ValueFunctionPlayer +from catanatron.players.minimax import AlphaBetaPlayer, SameTurnAlphaBetaPlayer +from catanatron.players.mcts import MCTSPlayer +from catanatron.players.playouts import GreedyPlayoutsPlayer + +# LLM players (optional - may not be available if pydantic-ai not installed) +try: + from catanatron.players.llm_player import ( + PydanticAIPlayer, + LLMAlphaBetaPlayer, + LLMMCTSPlayer, + LLMValuePlayer, + LLMPlayer, # Alias for PydanticAIPlayer + ) + + LLM_PLAYERS_AVAILABLE = True +except ImportError: + LLM_PLAYERS_AVAILABLE = False + PydanticAIPlayer = None + LLMAlphaBetaPlayer = None + LLMMCTSPlayer = None + LLMValuePlayer = None + LLMPlayer = None + +__all__ = [ + # Base + "Player", + "SimplePlayer", + "RandomPlayer", + "HumanPlayer", + "Color", + # Strategy + "WeightedRandomPlayer", + "VictoryPointPlayer", + "ValueFunctionPlayer", + "AlphaBetaPlayer", + "SameTurnAlphaBetaPlayer", + "MCTSPlayer", + "GreedyPlayoutsPlayer", + # LLM (may be None if not available) + "PydanticAIPlayer", + "LLMAlphaBetaPlayer", + "LLMMCTSPlayer", + "LLMValuePlayer", + "LLMPlayer", + "LLM_PLAYERS_AVAILABLE", +] diff --git a/catanatron/catanatron/players/llm/__init__.py b/catanatron/catanatron/players/llm/__init__.py new file mode 100644 index 00000000..b6fa89f9 --- /dev/null +++ b/catanatron/catanatron/players/llm/__init__.py @@ -0,0 +1,20 @@ +""" +LLM-powered player implementations using PydanticAI. + +This package provides LLM-based players that can use existing strategy players +(AlphaBetaPlayer, MCTSPlayer, etc.) as advisors while making decisions via LLM. +""" + +from catanatron.players.llm.base import BaseLLMPlayer, CatanDependencies +from catanatron.players.llm.output_types import ActionOutput, ActionByIndex +from catanatron.players.llm.state_formatter import StateFormatter +from catanatron.players.llm.history import ConversationHistoryManager + +__all__ = [ + "BaseLLMPlayer", + "CatanDependencies", + "ActionOutput", + "ActionByIndex", + "StateFormatter", + "ConversationHistoryManager", +] diff --git a/catanatron/catanatron/players/llm/base.py b/catanatron/catanatron/players/llm/base.py new file mode 100644 index 00000000..4f825405 --- /dev/null +++ b/catanatron/catanatron/players/llm/base.py @@ -0,0 +1,287 @@ +""" +Base class for LLM-powered Catan players using PydanticAI. + +This module provides: +- CatanDependencies: Dataclass for dependency injection into the agent +- BaseLLMPlayer: Abstract base class for LLM players with strategy advisor support +""" + +from dataclasses import dataclass +from typing import Optional, List, Literal, Any + +from pydantic_ai import Agent, RunContext + +from catanatron.game import Game +from catanatron.models.player import Player, Color +from catanatron.models.enums import Action, ActionType + +from catanatron.players.llm.output_types import ActionOutput, ActionByIndex +from catanatron.players.llm.history import ConversationHistoryManager +from catanatron.players.llm.state_formatter import StateFormatter + +import logfire + + +@dataclass +class CatanDependencies: + """ + Dependencies injected into the PydanticAI agent via RunContext. + + This provides the agent with all necessary game state and context + for making decisions. + """ + + color: Color + game: Game + playable_actions: List[Action] + strategy_recommendation: Optional[Action] + strategy_reasoning: Optional[str] + turn_number: int + is_my_turn: bool + + +# System prompt for the Catan agent +CATAN_SYSTEM_PROMPT = """You are an expert Settlers of Catan player. Your goal is to reach 10 victory points before your opponents. + +## Game Rules Summary +- Victory points come from: settlements (1 VP), cities (2 VP), longest road (2 VP), largest army (2 VP), victory point cards (1 VP each) +- Resources: Wood, Brick, Sheep, Wheat, Ore +- Building costs: + - Road: 1 Wood + 1 Brick + - Settlement: 1 Wood + 1 Brick + 1 Sheep + 1 Wheat + - City: 2 Wheat + 3 Ore + - Development Card: 1 Sheep + 1 Wheat + 1 Ore + +## Strategy Tips +- Diversify resource production by building on different numbers (6 and 8 are best) +- Secure important intersection spots early in the game +- Build towards valuable port locations for better trading rates +- Consider blocking opponents' expansion paths +- Time development card plays strategically (knights before rolling if robber is on you) +- Balance between expansion and resource accumulation + +## Decision Making +Use the available tools to analyze the game state before making decisions. +Consider both immediate gains and long-term strategy. +If a strategy advisor recommendation is provided, consider it but you may choose differently based on your analysis. + +Always return a valid action from the available options.""" + + +class BaseLLMPlayer(Player): + """ + Base class for LLM-powered players. + + This class can be combined with strategy players via multiple inheritance + to use their decide() method as a recommendation source. + + Example: + class LLMAlphaBetaPlayer(BaseLLMPlayer, AlphaBetaPlayer): + pass + + The LLM will receive the AlphaBetaPlayer's recommendation as context + but can choose to follow it or make a different decision. + """ + + def __init__( + self, + color: Color, + model: str = "anthropic:claude-sonnet-4-20250514", + output_mode: Literal["index", "structured"] = "index", + **strategy_kwargs, + ): + """ + Initialize the LLM player. + + Args: + color: Player color + model: PydanticAI model string (e.g., "anthropic:claude-sonnet-4-20250514", "openai:gpt-4o") + output_mode: "index" for fast mode, "structured" for detailed action types + **strategy_kwargs: Arguments passed to parent strategy player (e.g., depth for AlphaBeta) + """ + # Initialize parent player(s) + super().__init__(color, **strategy_kwargs) + + self.model = model + self.output_mode = output_mode + self.history_manager = ConversationHistoryManager() + + # Create the agent - defer to allow subclasses to customize + self.agent = self._create_agent() + + def _create_agent(self) -> Agent: + """Create the PydanticAI agent with tools.""" + from catanatron.players.llm.tools import register_tools + + # Use index-based output for simplicity and reliability + agent = Agent( + self.model, + deps_type=CatanDependencies, + output_type=ActionByIndex, + system_prompt=CATAN_SYSTEM_PROMPT, + ) + + # Register all tools + register_tools(agent) + + return agent + + def _get_strategy_recommendation( + self, game: Game, playable_actions: List[Action] + ) -> tuple[Optional[Action], Optional[str]]: + """ + Get recommendation from parent strategy player. + + This method traverses the MRO to find a parent class with a decide() + method (other than BaseLLMPlayer and Player base class). + + Returns: + Tuple of (recommended_action, reasoning_string) + """ + for cls in type(self).__mro__: + # Skip this class and the base Player class + if cls is BaseLLMPlayer or cls is Player: + continue + + # Check if this is a Player subclass with its own decide method + if issubclass(cls, Player) and "decide" in cls.__dict__: + try: + recommendation = cls.decide(self, game, playable_actions) + reasoning = self._explain_recommendation(recommendation, game, cls) + return recommendation, reasoning + except Exception as e: + # If strategy player fails, continue without recommendation + return None, f"Strategy advisor failed: {e}" + + return None, None + + def _explain_recommendation( + self, recommendation: Action, game: Game, strategy_cls: type + ) -> str: + """Generate explanation for why the strategy player chose this action.""" + action_type = recommendation.action_type.value + value = recommendation.value + + explanation = f"{strategy_cls.__name__} recommends: {action_type}" + + if value is not None: + if action_type == "BUILD_SETTLEMENT": + explanation += f" at node {value}" + elif action_type == "BUILD_ROAD": + explanation += f" on edge {value}" + elif action_type == "BUILD_CITY": + explanation += f" upgrading node {value}" + elif action_type == "MOVE_ROBBER": + coord, victim = value + explanation += f" to {coord}" + if victim: + explanation += f", stealing from {victim.value}" + elif action_type in ("PLAY_YEAR_OF_PLENTY", "PLAY_MONOPOLY"): + explanation += f" for {value}" + + return explanation + + def _build_prompt(self, game: Game) -> str: + """Build the user prompt for the current decision.""" + state = game.state + prompt_parts = [] + + # Current phase + prompt_parts.append(f"Current phase: {state.current_prompt.value}") + prompt_parts.append(f"Turn number: {state.num_turns}") + + # Trade context if applicable + if state.is_resolving_trade: + offer = state.current_trade[:5] + ask = state.current_trade[5:10] + prompt_parts.append(f"Active trade - Offering: {offer}, Asking: {ask}") + + prompt_parts.append( + "\nUse the available tools to understand the game state, then choose your action." + ) + prompt_parts.append( + "Return the index of your chosen action from the available actions list." + ) + + return "\n".join(prompt_parts) + + def _resolve_action( + self, output: ActionByIndex, playable_actions: List[Action] + ) -> Action: + """ + Resolve the LLM output to a valid Action. + + Args: + output: The ActionByIndex from the LLM + playable_actions: List of valid actions + + Returns: + The selected Action, or first action if index is invalid + """ + if isinstance(output, ActionByIndex): + idx = output.action_index + if 0 <= idx < len(playable_actions): + return playable_actions[idx] + # Invalid index, fall back to first action + return playable_actions[0] + + # For structured output mode (future enhancement) + # Would need to match against playable_actions + return playable_actions[0] + + def decide(self, game: Game, playable_actions: List[Action]) -> Action: + """ + Make a decision using the LLM agent. + + This is the main entry point called by the game engine. + """ + # 1. Get strategy recommendation from parent (if any) + recommendation, reasoning = self._get_strategy_recommendation( + game, playable_actions + ) + + # 2. Check for turn boundary, manage history + current_turn = game.state.num_turns + if self.history_manager.is_new_turn(current_turn): + self.history_manager.clear() + self.history_manager.set_turn(current_turn) + + # 3. Build dependencies + deps = CatanDependencies( + color=self.color, + game=game, + playable_actions=playable_actions, + strategy_recommendation=recommendation, + strategy_reasoning=reasoning, + turn_number=current_turn, + is_my_turn=game.state.current_turn_index + == game.state.color_to_index[self.color], + ) + + # 4. Run agent with history + try: + result = self.agent.run_sync( + self._build_prompt(game), + deps=deps, + message_history=self.history_manager.get_messages(), + ) + + # 5. Update history + self.history_manager.update(result.all_messages()) + + # 6. Map output to Action + action = self._resolve_action(result.output, playable_actions) + logfire.info(f"LLM player {self.color} chose action {action.action_type}", turn_number=current_turn) + return action + + except Exception as e: + # On any error, fall back to strategy recommendation or first action + logfire.error(f"LLM player {self.color} error: {e}", turn_number=current_turn) + if recommendation is not None: + return recommendation + return playable_actions[0] + + def reset_state(self): + """Reset state between games.""" + super().reset_state() + self.history_manager.clear() diff --git a/catanatron/catanatron/players/llm/history.py b/catanatron/catanatron/players/llm/history.py new file mode 100644 index 00000000..a683fb88 --- /dev/null +++ b/catanatron/catanatron/players/llm/history.py @@ -0,0 +1,102 @@ +""" +Conversation history management for LLM players. + +Manages message history across multiple decide() calls within a turn, +with automatic clearing at turn boundaries. +""" + +from typing import List, Any + + +class ConversationHistoryManager: + """ + Manages conversation history for LLM agent across multiple calls. + + In Catan, a single "turn" may involve multiple decide() calls: + - Rolling dice + - Building multiple things + - Trading with other players + - Playing development cards + + This manager: + - Keeps conversation context within a turn for coherent decision making + - Clears history at turn boundaries to prevent context overflow + - Provides hooks for future extensions (summarization, etc.) + """ + + def __init__(self): + """Initialize the history manager.""" + self.messages: List[Any] = [] # PydanticAI message objects + self.current_turn: int = -1 + + def is_new_turn(self, turn_number: int) -> bool: + """ + Check if the given turn number represents a new turn. + + Args: + turn_number: The current game turn number + + Returns: + True if this is a different turn than the last recorded one + """ + return turn_number != self.current_turn + + def set_turn(self, turn_number: int) -> None: + """ + Set the current turn number. + + Args: + turn_number: The turn number to track + """ + self.current_turn = turn_number + + def clear(self) -> None: + """Clear all stored messages.""" + self.messages = [] + + def get_messages(self) -> List[Any]: + """ + Get the current message history. + + Returns: + List of PydanticAI message objects + """ + return self.messages + + def update(self, new_messages: List[Any]) -> None: + """ + Update the stored messages with new conversation. + + Args: + new_messages: The complete message history from the latest agent run + """ + self.messages = new_messages + + def add_message(self, message: Any) -> None: + """ + Add a single message to the history. + + Args: + message: A message to append + """ + self.messages.append(message) + + @property + def message_count(self) -> int: + """Get the number of messages in history.""" + return len(self.messages) + + def trim_to_last_n(self, n: int) -> None: + """ + Keep only the last N messages. + + Useful for preventing context overflow in long games. + + Args: + n: Number of recent messages to keep + """ + if len(self.messages) > n: + self.messages = self.messages[-n:] + + def __repr__(self) -> str: + return f"ConversationHistoryManager(turn={self.current_turn}, messages={len(self.messages)})" diff --git a/catanatron/catanatron/players/llm/output_types.py b/catanatron/catanatron/players/llm/output_types.py new file mode 100644 index 00000000..9b1a1a73 --- /dev/null +++ b/catanatron/catanatron/players/llm/output_types.py @@ -0,0 +1,239 @@ +""" +Pydantic models for LLM action outputs. + +Supports two modes: +1. Index mode: LLM returns an index into playable_actions (fast, simple) +2. Structured mode: LLM returns typed action with parameters (better for logging/debugging) +""" + +from pydantic import BaseModel, Field +from typing import Union, Literal, Optional, Tuple, List + + +# ============= Index-based Output (Fast Mode) ============= + + +class ActionByIndex(BaseModel): + """Fast mode: just return the action index from playable_actions list.""" + + action_index: int = Field( + description="Index into the playable_actions list (0-based)" + ) + reasoning: Optional[str] = Field( + default=None, description="Brief explanation of why this action was chosen" + ) + + +# ============= Structured Outputs (Detailed Mode) ============= + + +class RollAction(BaseModel): + """Roll the dice to start the turn.""" + + action_type: Literal["ROLL"] = "ROLL" + reasoning: str = Field(description="Why rolling now") + + +class EndTurnAction(BaseModel): + """End the current turn.""" + + action_type: Literal["END_TURN"] = "END_TURN" + reasoning: str = Field(description="Why ending turn now") + + +class BuildSettlementAction(BaseModel): + """Build a settlement at a node.""" + + action_type: Literal["BUILD_SETTLEMENT"] = "BUILD_SETTLEMENT" + node_id: int = Field(description="The node ID where to build the settlement") + reasoning: str = Field(description="Why building at this location") + + +class BuildCityAction(BaseModel): + """Upgrade a settlement to a city.""" + + action_type: Literal["BUILD_CITY"] = "BUILD_CITY" + node_id: int = Field(description="The node ID of the settlement to upgrade") + reasoning: str = Field(description="Why upgrading this settlement") + + +class BuildRoadAction(BaseModel): + """Build a road between two nodes.""" + + action_type: Literal["BUILD_ROAD"] = "BUILD_ROAD" + edge: Tuple[int, int] = Field(description="Edge as (node1_id, node2_id)") + reasoning: str = Field(description="Why building this road") + + +class BuyDevelopmentCardAction(BaseModel): + """Buy a development card from the bank.""" + + action_type: Literal["BUY_DEVELOPMENT_CARD"] = "BUY_DEVELOPMENT_CARD" + reasoning: str = Field(description="Why buying a development card") + + +class MoveRobberAction(BaseModel): + """Move the robber to a new tile and optionally steal from a player.""" + + action_type: Literal["MOVE_ROBBER"] = "MOVE_ROBBER" + coordinate: Tuple[int, int, int] = Field( + description="Tile coordinate (x, y, z) to move robber to" + ) + victim_color: Optional[str] = Field( + default=None, description="Color of player to steal from, or None" + ) + reasoning: str = Field(description="Why moving robber here and stealing from this player") + + +class DiscardAction(BaseModel): + """Discard resources when over the limit after a 7 is rolled.""" + + action_type: Literal["DISCARD"] = "DISCARD" + resources: Optional[List[str]] = Field( + default=None, + description="List of resources to discard, or None for random discard", + ) + reasoning: str = Field(description="Why discarding these resources") + + +# ============= Development Card Actions ============= + + +class PlayKnightCardAction(BaseModel): + """Play a knight development card.""" + + action_type: Literal["PLAY_KNIGHT_CARD"] = "PLAY_KNIGHT_CARD" + reasoning: str = Field(description="Why playing knight now") + + +class PlayYearOfPlentyAction(BaseModel): + """Play Year of Plenty to take 2 resources from the bank.""" + + action_type: Literal["PLAY_YEAR_OF_PLENTY"] = "PLAY_YEAR_OF_PLENTY" + resource1: str = Field(description="First resource to take") + resource2: str = Field(description="Second resource to take") + reasoning: str = Field(description="Why choosing these resources") + + +class PlayMonopolyAction(BaseModel): + """Play Monopoly to take all of one resource type from other players.""" + + action_type: Literal["PLAY_MONOPOLY"] = "PLAY_MONOPOLY" + resource: str = Field(description="Resource type to monopolize") + reasoning: str = Field(description="Why choosing this resource") + + +class PlayRoadBuildingAction(BaseModel): + """Play Road Building to build 2 free roads.""" + + action_type: Literal["PLAY_ROAD_BUILDING"] = "PLAY_ROAD_BUILDING" + reasoning: str = Field(description="Why playing road building now") + + +# ============= Trade Actions ============= + + +class MaritimeTradeAction(BaseModel): + """Trade with the bank at port rates.""" + + action_type: Literal["MARITIME_TRADE"] = "MARITIME_TRADE" + give_resources: List[str] = Field( + description="Resources to give (2-4 of same type)" + ) + receive_resource: str = Field(description="Resource to receive") + reasoning: str = Field(description="Why making this trade") + + +class OfferTradeAction(BaseModel): + """Offer a trade to other players.""" + + action_type: Literal["OFFER_TRADE"] = "OFFER_TRADE" + offering: List[int] = Field( + description="Resources offering as [wood, brick, sheep, wheat, ore] counts" + ) + asking: List[int] = Field( + description="Resources asking for as [wood, brick, sheep, wheat, ore] counts" + ) + reasoning: str = Field(description="Why proposing this trade") + + +class AcceptTradeAction(BaseModel): + """Accept a trade offer from another player.""" + + action_type: Literal["ACCEPT_TRADE"] = "ACCEPT_TRADE" + reasoning: str = Field(description="Why accepting this trade") + + +class RejectTradeAction(BaseModel): + """Reject a trade offer from another player.""" + + action_type: Literal["REJECT_TRADE"] = "REJECT_TRADE" + reasoning: str = Field(description="Why rejecting this trade") + + +class ConfirmTradeAction(BaseModel): + """Confirm a trade with a specific player who accepted.""" + + action_type: Literal["CONFIRM_TRADE"] = "CONFIRM_TRADE" + trading_partner_color: str = Field( + description="Color of the player to trade with" + ) + reasoning: str = Field(description="Why trading with this player") + + +class CancelTradeAction(BaseModel): + """Cancel the current trade offer.""" + + action_type: Literal["CANCEL_TRADE"] = "CANCEL_TRADE" + reasoning: str = Field(description="Why canceling the trade") + + +# ============= Union Type for All Actions ============= + +StructuredActionOutput = Union[ + RollAction, + EndTurnAction, + BuildSettlementAction, + BuildCityAction, + BuildRoadAction, + BuyDevelopmentCardAction, + MoveRobberAction, + DiscardAction, + PlayKnightCardAction, + PlayYearOfPlentyAction, + PlayMonopolyAction, + PlayRoadBuildingAction, + MaritimeTradeAction, + OfferTradeAction, + AcceptTradeAction, + RejectTradeAction, + ConfirmTradeAction, + CancelTradeAction, +] + +# The main output type - supports both modes +ActionOutput = Union[ActionByIndex, StructuredActionOutput] + + +# ============= Helper to map action type string to model ============= + +ACTION_TYPE_TO_MODEL = { + "ROLL": RollAction, + "END_TURN": EndTurnAction, + "BUILD_SETTLEMENT": BuildSettlementAction, + "BUILD_CITY": BuildCityAction, + "BUILD_ROAD": BuildRoadAction, + "BUY_DEVELOPMENT_CARD": BuyDevelopmentCardAction, + "MOVE_ROBBER": MoveRobberAction, + "DISCARD": DiscardAction, + "PLAY_KNIGHT_CARD": PlayKnightCardAction, + "PLAY_YEAR_OF_PLENTY": PlayYearOfPlentyAction, + "PLAY_MONOPOLY": PlayMonopolyAction, + "PLAY_ROAD_BUILDING": PlayRoadBuildingAction, + "MARITIME_TRADE": MaritimeTradeAction, + "OFFER_TRADE": OfferTradeAction, + "ACCEPT_TRADE": AcceptTradeAction, + "REJECT_TRADE": RejectTradeAction, + "CONFIRM_TRADE": ConfirmTradeAction, + "CANCEL_TRADE": CancelTradeAction, +} diff --git a/catanatron/catanatron/players/llm/state_formatter.py b/catanatron/catanatron/players/llm/state_formatter.py new file mode 100644 index 00000000..5e46b95b --- /dev/null +++ b/catanatron/catanatron/players/llm/state_formatter.py @@ -0,0 +1,394 @@ +""" +State formatting utilities for LLM-readable game state. + +Converts Catanatron game state into structured dictionaries and +natural language descriptions suitable for LLM consumption. +""" + +from typing import Dict, List, Any, Optional + +from catanatron.game import Game +from catanatron.models.player import Color +from catanatron.models.enums import ( + Action, + ActionType, + WOOD, + BRICK, + SHEEP, + WHEAT, + ORE, + RESOURCES, + SETTLEMENT, + CITY, + ROAD, +) +from catanatron.state_functions import ( + player_key, + get_player_freqdeck, + player_num_resource_cards, + get_dev_cards_in_hand, + get_played_dev_cards, + get_actual_victory_points, + get_visible_victory_points, + get_longest_road_length, + get_longest_road_color, + get_largest_army, + player_has_rolled, +) + + +RESOURCE_NAMES = ["WOOD", "BRICK", "SHEEP", "WHEAT", "ORE"] + + +class StateFormatter: + """ + Utility class for formatting game state for LLM consumption. + + Provides methods to convert game state to: + - Structured dictionaries (for tool outputs) + - Natural language summaries (for prompts) + - Action descriptions + """ + + @staticmethod + def format_full_state(game: Game, my_color: Color) -> Dict[str, Any]: + """ + Convert complete game state to a structured dictionary. + + This is the main state representation used by tools. + """ + state = game.state + my_key = player_key(state, my_color) + + return { + "turn": state.num_turns, + "phase": state.current_prompt.value, + "my_color": my_color.value, + "is_initial_build_phase": state.is_initial_build_phase, + "my_state": StateFormatter._format_player_state(state, my_color, my_key, is_self=True), + "opponents": StateFormatter._format_opponents(state, my_color), + "board": StateFormatter._format_board_state(state, my_color), + "bank": StateFormatter._format_bank(state), + "special_achievements": StateFormatter._format_achievements(state), + "trade_state": StateFormatter._format_trade_state(state), + } + + @staticmethod + def _format_player_state( + state, color: Color, key: str, is_self: bool = False + ) -> Dict[str, Any]: + """Format a single player's state.""" + result = { + "color": color.value, + "visible_victory_points": get_visible_victory_points(state, color), + "num_resource_cards": player_num_resource_cards(state, color), + "num_dev_cards": get_dev_cards_in_hand(state, color), + "played_knights": get_played_dev_cards(state, color, "KNIGHT"), + "longest_road_length": get_longest_road_length(state, color), + "has_longest_road": state.player_state.get(f"{key}_HAS_ROAD", False), + "has_largest_army": state.player_state.get(f"{key}_HAS_ARMY", False), + "settlements_available": state.player_state.get(f"{key}_SETTLEMENTS_AVAILABLE", 0), + "cities_available": state.player_state.get(f"{key}_CITIES_AVAILABLE", 0), + "roads_available": state.player_state.get(f"{key}_ROADS_AVAILABLE", 0), + } + + # Add private information only for self + if is_self: + result["actual_victory_points"] = get_actual_victory_points(state, color) + result["resources"] = { + "wood": state.player_state.get(f"{key}_WOOD_IN_HAND", 0), + "brick": state.player_state.get(f"{key}_BRICK_IN_HAND", 0), + "sheep": state.player_state.get(f"{key}_SHEEP_IN_HAND", 0), + "wheat": state.player_state.get(f"{key}_WHEAT_IN_HAND", 0), + "ore": state.player_state.get(f"{key}_ORE_IN_HAND", 0), + } + result["development_cards"] = { + "knight": state.player_state.get(f"{key}_KNIGHT_IN_HAND", 0), + "year_of_plenty": state.player_state.get(f"{key}_YEAR_OF_PLENTY_IN_HAND", 0), + "monopoly": state.player_state.get(f"{key}_MONOPOLY_IN_HAND", 0), + "road_building": state.player_state.get(f"{key}_ROAD_BUILDING_IN_HAND", 0), + "victory_point": state.player_state.get(f"{key}_VICTORY_POINT_IN_HAND", 0), + } + result["has_rolled"] = player_has_rolled(state, color) + + # Buildings + buildings = state.buildings_by_color.get(color, {}) + result["settlements"] = buildings.get(SETTLEMENT, []) + result["cities"] = buildings.get(CITY, []) + result["num_roads"] = len(buildings.get(ROAD, [])) + + return result + + @staticmethod + def _format_opponents(state, my_color: Color) -> List[Dict[str, Any]]: + """Format information about opponents.""" + opponents = [] + for color in state.colors: + if color == my_color: + continue + key = player_key(state, color) + opponents.append( + StateFormatter._format_player_state(state, color, key, is_self=False) + ) + return opponents + + @staticmethod + def _format_board_state(state, my_color: Color) -> Dict[str, Any]: + """Format board-related information.""" + board = state.board + + return { + "robber_coordinate": board.robber_coordinate, + "buildable_settlement_nodes": list(board.buildable_node_ids(my_color)), + "buildable_road_edges": [ + list(edge) for edge in board.buildable_edges(my_color) + ], + "my_ports": list(board.get_player_port_resources(my_color)), + } + + @staticmethod + def _format_bank(state) -> Dict[str, Any]: + """Format bank resource availability.""" + freqdeck = state.resource_freqdeck + return { + "resources": { + "wood": freqdeck[0], + "brick": freqdeck[1], + "sheep": freqdeck[2], + "wheat": freqdeck[3], + "ore": freqdeck[4], + }, + "dev_cards_remaining": len(state.development_listdeck), + } + + @staticmethod + def _format_achievements(state) -> Dict[str, Any]: + """Format special achievements (longest road, largest army).""" + longest_road_color = get_longest_road_color(state) + largest_army_color, largest_army_size = get_largest_army(state) + + return { + "longest_road_holder": longest_road_color.value if longest_road_color else None, + "largest_army_holder": largest_army_color.value if largest_army_color else None, + "largest_army_size": largest_army_size, + } + + @staticmethod + def _format_trade_state(state) -> Dict[str, Any]: + """Format current trade state if any.""" + if not state.is_resolving_trade: + return {"is_trading": False} + + current_trade = state.current_trade + return { + "is_trading": True, + "offering": { + "wood": current_trade[0], + "brick": current_trade[1], + "sheep": current_trade[2], + "wheat": current_trade[3], + "ore": current_trade[4], + }, + "asking": { + "wood": current_trade[5], + "brick": current_trade[6], + "sheep": current_trade[7], + "wheat": current_trade[8], + "ore": current_trade[9], + }, + "acceptees": list(state.acceptees) if state.acceptees else [], + } + + @staticmethod + def format_for_prompt(game: Game, my_color: Color) -> str: + """ + Create a natural language summary suitable for LLM prompts. + + This is a concise overview rather than full state dump. + """ + state = game.state + my_key = player_key(state, my_color) + + lines = [] + lines.append(f"=== GAME STATE (Turn {state.num_turns}) ===") + lines.append(f"You are: {my_color.value}") + lines.append(f"Phase: {state.current_prompt.value}") + lines.append("") + + # My state + lines.append("YOUR STATUS:") + vps = get_actual_victory_points(state, my_color) + lines.append(f" Victory Points: {vps}") + + # Resources + wood = state.player_state.get(f"{my_key}_WOOD_IN_HAND", 0) + brick = state.player_state.get(f"{my_key}_BRICK_IN_HAND", 0) + sheep = state.player_state.get(f"{my_key}_SHEEP_IN_HAND", 0) + wheat = state.player_state.get(f"{my_key}_WHEAT_IN_HAND", 0) + ore = state.player_state.get(f"{my_key}_ORE_IN_HAND", 0) + lines.append(f" Resources: Wood({wood}), Brick({brick}), Sheep({sheep}), Wheat({wheat}), Ore({ore})") + + # Dev cards + knights = state.player_state.get(f"{my_key}_KNIGHT_IN_HAND", 0) + yop = state.player_state.get(f"{my_key}_YEAR_OF_PLENTY_IN_HAND", 0) + monopoly = state.player_state.get(f"{my_key}_MONOPOLY_IN_HAND", 0) + rb = state.player_state.get(f"{my_key}_ROAD_BUILDING_IN_HAND", 0) + vp_cards = state.player_state.get(f"{my_key}_VICTORY_POINT_IN_HAND", 0) + if any([knights, yop, monopoly, rb, vp_cards]): + lines.append(f" Dev Cards: Knight({knights}), YoP({yop}), Monopoly({monopoly}), RoadBuilding({rb}), VP({vp_cards})") + + # Buildings + buildings = state.buildings_by_color.get(my_color, {}) + settlements = len(buildings.get(SETTLEMENT, [])) + cities = len(buildings.get(CITY, [])) + roads = len(buildings.get(ROAD, [])) + lines.append(f" Buildings: {settlements} settlements, {cities} cities, {roads} roads") + lines.append("") + + # Opponents + lines.append("OPPONENTS:") + for color in state.colors: + if color == my_color: + continue + other_key = player_key(state, color) + other_vps = get_visible_victory_points(state, color) + other_cards = player_num_resource_cards(state, color) + other_dev = get_dev_cards_in_hand(state, color) + lines.append(f" {color.value}: {other_vps} VPs, {other_cards} resource cards, {other_dev} dev cards") + + lines.append("") + + # Trade state if applicable + if state.is_resolving_trade: + offer = state.current_trade[:5] + ask = state.current_trade[5:10] + lines.append(f"ACTIVE TRADE: Offering {offer}, Asking {ask}") + lines.append("") + + return "\n".join(lines) + + @staticmethod + def format_action(action: Action, index: int) -> Dict[str, Any]: + """Format a single action for display to LLM.""" + action_type = action.action_type.value + value = action.value + + result = { + "index": index, + "type": action_type, + "description": StateFormatter._describe_action(action), + } + + if value is not None: + result["value"] = StateFormatter._serialize_value(value) + + return result + + @staticmethod + def _describe_action(action: Action) -> str: + """Generate human-readable description of an action.""" + action_type = action.action_type + value = action.value + + descriptions = { + ActionType.ROLL: "Roll the dice", + ActionType.END_TURN: "End your turn", + ActionType.BUY_DEVELOPMENT_CARD: "Buy a development card (costs: 1 sheep, 1 wheat, 1 ore)", + ActionType.PLAY_KNIGHT_CARD: "Play Knight card (move robber)", + ActionType.PLAY_ROAD_BUILDING: "Play Road Building card (build 2 free roads)", + ActionType.CANCEL_TRADE: "Cancel the trade offer", + } + + if action_type in descriptions: + return descriptions[action_type] + + if action_type == ActionType.BUILD_SETTLEMENT: + return f"Build settlement at node {value}" + + if action_type == ActionType.BUILD_CITY: + return f"Upgrade settlement to city at node {value}" + + if action_type == ActionType.BUILD_ROAD: + return f"Build road between nodes {value[0]} and {value[1]}" + + if action_type == ActionType.MOVE_ROBBER: + coord, victim = value + if victim: + return f"Move robber to {coord} and steal from {victim.value}" + return f"Move robber to {coord} (no one to steal from)" + + if action_type == ActionType.DISCARD: + if value: + return f"Discard resources: {value}" + return "Discard resources (random selection)" + + if action_type == ActionType.PLAY_YEAR_OF_PLENTY: + return f"Play Year of Plenty: take {value[0]} and {value[1]}" + + if action_type == ActionType.PLAY_MONOPOLY: + return f"Play Monopoly: take all {value} from opponents" + + if action_type == ActionType.MARITIME_TRADE: + give = [r for r in value[:4] if r is not None] + get = value[4] + return f"Maritime trade: give {give} for {get}" + + if action_type == ActionType.OFFER_TRADE: + offer = value[:5] + ask = value[5:] + return f"Offer trade: give {StateFormatter._freqdeck_to_str(offer)} for {StateFormatter._freqdeck_to_str(ask)}" + + if action_type == ActionType.ACCEPT_TRADE: + return "Accept the trade offer" + + if action_type == ActionType.REJECT_TRADE: + return "Reject the trade offer" + + if action_type == ActionType.CONFIRM_TRADE: + partner = value[10] if len(value) > 10 else "unknown" + return f"Confirm trade with {partner}" + + return f"{action_type.value}" + + @staticmethod + def _serialize_value(value: Any) -> Any: + """Serialize action value for JSON output.""" + if value is None: + return None + if isinstance(value, (int, str, bool, float)): + return value + if isinstance(value, tuple): + return list(value) + if isinstance(value, list): + return value + if hasattr(value, "value"): # Enum + return value.value + return str(value) + + @staticmethod + def _freqdeck_to_str(freqdeck: List[int]) -> str: + """Convert a freqdeck to readable string.""" + parts = [] + for i, count in enumerate(freqdeck): + if count > 0: + parts.append(f"{count} {RESOURCE_NAMES[i].lower()}") + return ", ".join(parts) if parts else "nothing" + + @staticmethod + def format_strategy_insight( + recommendation: Optional[Action], + reasoning: Optional[str], + strategy_name: str = "Strategy Advisor", + ) -> str: + """Format parent player's recommendation for the LLM.""" + if recommendation is None: + return "" + + desc = StateFormatter._describe_action(recommendation) + return f""" +STRATEGY ADVISOR RECOMMENDATION: +The {strategy_name} suggests: {desc} +Reasoning: {reasoning or "No detailed reasoning available"} + +You may follow this recommendation or choose differently based on your analysis. +""" diff --git a/catanatron/catanatron/players/llm/tools.py b/catanatron/catanatron/players/llm/tools.py new file mode 100644 index 00000000..d5ca40d3 --- /dev/null +++ b/catanatron/catanatron/players/llm/tools.py @@ -0,0 +1,539 @@ +""" +PydanticAI tools for the Catan LLM agent. + +Provides 8 tools for game state analysis and decision making: +1. get_game_state - Comprehensive game state +2. get_available_actions - List of valid actions +3. evaluate_action - Evaluate immediate impact of an action +4. simulate_action - Simulate future states +5. get_opponent_info - Opponent analysis +6. get_resource_production - Production probabilities +7. get_winning_probability - Win probability estimate +8. analyze_board - Strategic board analysis +""" + +from typing import List, Dict, Any +from pydantic_ai import Agent, RunContext + +from catanatron.players.llm.base import CatanDependencies + +from catanatron.state_functions import ( + player_key, + get_actual_victory_points, + get_visible_victory_points, + get_player_freqdeck, + player_num_resource_cards, + get_dev_cards_in_hand, + get_played_dev_cards, + get_longest_road_length, + get_longest_road_color, + get_largest_army, + get_player_buildings, +) +from catanatron.models.enums import RESOURCES, SETTLEMENT, CITY, ROAD +from catanatron.models.map import LandTile +from catanatron.players.llm.state_formatter import StateFormatter + + +def register_tools(agent: Agent) -> None: + """ + Register all tools with the agent. + + Args: + agent: The PydanticAI agent to register tools on + """ + + @agent.tool + def get_game_state(ctx: RunContext[CatanDependencies]) -> Dict[str, Any]: + """ + Get comprehensive current game state including your resources, + buildings, opponents, and board information. + + Use this to understand the overall game situation before making decisions. + """ + return StateFormatter.format_full_state(ctx.deps.game, ctx.deps.color) + + @agent.tool + def get_available_actions(ctx: RunContext[CatanDependencies]) -> List[Dict[str, Any]]: + """ + Get list of all valid actions you can take right now. + + Each action has an index, type, and description. + Return the index of your chosen action. + """ + actions = [] + for i, action in enumerate(ctx.deps.playable_actions): + actions.append(StateFormatter.format_action(action, i)) + return actions + + # @agent.tool + # def get_strategy_recommendation(ctx: RunContext[CatanDependencies]) -> Dict[str, Any]: + # """ + # Get the strategy advisor's recommended action and reasoning. + + # The strategy advisor uses algorithmic analysis (like AlphaBeta search) + # to suggest the best action. You can follow or override this recommendation. + # """ + # if ctx.deps.strategy_recommendation is None: + # return { + # "has_recommendation": False, + # "message": "No strategy advisor configured", + # } + + # # Find the index of the recommended action + # rec_index = None + # for i, action in enumerate(ctx.deps.playable_actions): + # if action == ctx.deps.strategy_recommendation: + # rec_index = i + # break + + # return { + # "has_recommendation": True, + # "recommended_action_index": rec_index, + # "action": StateFormatter.format_action(ctx.deps.strategy_recommendation, rec_index or 0), + # "reasoning": ctx.deps.strategy_reasoning or "No detailed reasoning available", + # } + + # @agent.tool + # def evaluate_action( + # ctx: RunContext[CatanDependencies], action_index: int + # ) -> Dict[str, Any]: + # """ + # Evaluate a specific action's immediate impact. + + # Args: + # action_index: Index of the action to evaluate (from get_available_actions) + + # Returns: + # Dictionary with VP change, resource changes, and other impacts. + # """ + # if action_index < 0 or action_index >= len(ctx.deps.playable_actions): + # return {"error": f"Invalid action index: {action_index}"} + + # action = ctx.deps.playable_actions[action_index] + # game = ctx.deps.game + # my_color = ctx.deps.color + + # # Get current state + # before_vps = get_actual_victory_points(game.state, my_color) + # before_resources = get_player_freqdeck(game.state, my_color) + + # # Simulate the action + # game_copy = game.copy() + # try: + # game_copy.execute(action) + # except Exception as e: + # return {"error": f"Failed to simulate action: {str(e)}"} + + # # Get new state + # after_vps = get_actual_victory_points(game_copy.state, my_color) + # after_resources = get_player_freqdeck(game_copy.state, my_color) + + # # Calculate changes + # resource_names = ["wood", "brick", "sheep", "wheat", "ore"] + # resource_changes = { + # name: after_resources[i] - before_resources[i] + # for i, name in enumerate(resource_names) + # } + + # return { + # "action": StateFormatter.format_action(action, action_index), + # "vp_change": after_vps - before_vps, + # "resource_changes": resource_changes, + # "new_resource_totals": { + # name: after_resources[i] for i, name in enumerate(resource_names) + # }, + # } + + # @agent.tool + # def simulate_action( + # ctx: RunContext[CatanDependencies], action_index: int + # ) -> Dict[str, Any]: + # """ + # Simulate taking an action and show the resulting game state. + + # This helps you see what the board will look like after the action. + + # Args: + # action_index: Index of the action to simulate + # """ + # if action_index < 0 or action_index >= len(ctx.deps.playable_actions): + # return {"error": f"Invalid action index: {action_index}"} + + # action = ctx.deps.playable_actions[action_index] + # game = ctx.deps.game + + # # Simulate the action + # game_copy = game.copy() + # try: + # game_copy.execute(action) + # except Exception as e: + # return {"error": f"Failed to simulate action: {str(e)}"} + + # # Return the new state summary + # return { + # "action_taken": StateFormatter.format_action(action, action_index), + # "resulting_state": StateFormatter.format_full_state(game_copy, ctx.deps.color), + # "new_available_actions_count": len(game_copy.playable_actions), + # } + + # @agent.tool + # def get_opponent_info(ctx: RunContext[CatanDependencies]) -> List[Dict[str, Any]]: + # """ + # Get detailed information about all opponents. + + # Includes their visible VPs, card counts, buildings, and achievements. + # """ + # opponents = [] + # state = ctx.deps.game.state + # my_color = ctx.deps.color + + # for color in state.colors: + # if color == my_color: + # continue + + # key = player_key(state, color) + # buildings = state.buildings_by_color.get(color, {}) + + # opponent_info = { + # "color": color.value, + # "visible_victory_points": get_visible_victory_points(state, color), + # "resource_card_count": player_num_resource_cards(state, color), + # "dev_card_count": get_dev_cards_in_hand(state, color), + # "played_knights": get_played_dev_cards(state, color, "KNIGHT"), + # "longest_road_length": get_longest_road_length(state, color), + # "has_longest_road": state.player_state.get(f"{key}_HAS_ROAD", False), + # "has_largest_army": state.player_state.get(f"{key}_HAS_ARMY", False), + # "settlements": len(buildings.get(SETTLEMENT, [])), + # "cities": len(buildings.get(CITY, [])), + # "roads": len(buildings.get(ROAD, [])), + # "threat_assessment": _assess_threat(state, color, my_color), + # } + # opponents.append(opponent_info) + + # # Sort by VPs descending + # opponents.sort(key=lambda x: x["visible_victory_points"], reverse=True) + # return opponents + + # @agent.tool + # def get_resource_production(ctx: RunContext[CatanDependencies]) -> Dict[str, Any]: + # """ + # Get expected resource production probabilities for all players. + + # Shows which resources each player is likely to receive on average per turn, + # accounting for settlements, cities, and the robber position. + # """ + # from catanatron.features import build_production_features + + # game = ctx.deps.game + # my_color = ctx.deps.color + + # # Use the production features from the game + # prod_fn = build_production_features(consider_robber=True) + # production = prod_fn(game, my_color) + + # # Format the production data + # result = {"my_production": {}, "opponent_production": {}} + + # resource_names = ["WOOD", "BRICK", "SHEEP", "WHEAT", "ORE"] + + # # My production (P0 in the features) + # for resource in resource_names: + # key = f"EFFECTIVE_P0_{resource}_PRODUCTION" + # result["my_production"][resource.lower()] = round(production.get(key, 0), 3) + + # # Calculate total for me + # result["my_production"]["total"] = round( + # sum(result["my_production"].values()), 3 + # ) + + # # Opponent production + # for i, color in enumerate(game.state.colors): + # if color == my_color: + # continue + # # Find the player index in the feature output + # player_idx = (i - game.state.colors.index(my_color)) % len(game.state.colors) + # opp_prod = {} + # for resource in resource_names: + # key = f"EFFECTIVE_P{player_idx}_{resource}_PRODUCTION" + # opp_prod[resource.lower()] = round(production.get(key, 0), 3) + # opp_prod["total"] = round(sum(opp_prod.values()), 3) + # result["opponent_production"][color.value] = opp_prod + + # return result + + # @agent.tool + # def get_winning_probability(ctx: RunContext[CatanDependencies]) -> Dict[str, Any]: + # """ + # Estimate winning probability based on current position. + + # Uses a heuristic value function to estimate each player's strength. + # """ + # game = ctx.deps.game + # state = game.state + # my_color = ctx.deps.color + + # # Calculate a simple score for each player + # scores = {} + # for color in state.colors: + # key = player_key(state, color) + + # # Base score from VPs + # vps = get_visible_victory_points(state, color) + # score = vps * 100 + + # # Add for resources (flexibility) + # cards = player_num_resource_cards(state, color) + # score += min(cards, 10) * 5 # Cap to avoid overvaluing large hands + + # # Add for dev cards + # dev_cards = get_dev_cards_in_hand(state, color) + # score += dev_cards * 15 + + # # Add for longest road/army + # if state.player_state.get(f"{key}_HAS_ROAD", False): + # score += 200 + # if state.player_state.get(f"{key}_HAS_ARMY", False): + # score += 200 + + # # Add for road length progress + # road_length = get_longest_road_length(state, color) + # if road_length >= 4: + # score += (road_length - 4) * 20 + + # # Add for army progress + # knights = get_played_dev_cards(state, color, "KNIGHT") + # if knights >= 2: + # score += (knights - 2) * 20 + + # scores[color.value] = score + + # # Convert to probabilities + # total_score = sum(scores.values()) + # probabilities = { + # color: round(score / total_score * 100, 1) if total_score > 0 else 25.0 + # for color, score in scores.items() + # } + + # my_vps = get_actual_victory_points(state, my_color) + + # return { + # "my_color": my_color.value, + # "my_victory_points": my_vps, + # "vps_to_win": game.vps_to_win, + # "vps_remaining": game.vps_to_win - my_vps, + # "estimated_win_probability": probabilities.get(my_color.value, 0), + # "all_probabilities": probabilities, + # "ranking": sorted( + # probabilities.items(), key=lambda x: x[1], reverse=True + # ), + # } + + @agent.tool + def analyze_board( + ctx: RunContext[CatanDependencies], focus: str + ) -> Dict[str, Any]: + """ + Analyze the board for strategic insights. + + Args: + focus: What to analyze. Options: + - 'expansion': Where can I build next, best spots + - 'blocking': How to block opponents' expansion + - 'ports': Port accessibility and trading options + - 'robber': Optimal robber placements + """ + game = ctx.deps.game + state = game.state + board = state.board + my_color = ctx.deps.color + + if focus == "expansion": + return _analyze_expansion(game, my_color) + elif focus == "blocking": + return _analyze_blocking(game, my_color) + elif focus == "ports": + return _analyze_ports(game, my_color) + elif focus == "robber": + return _analyze_robber(game, my_color) + else: + return { + "error": f"Unknown focus: {focus}", + "valid_options": ["expansion", "blocking", "ports", "robber"], + } + + +def _assess_threat(state, opponent_color, my_color) -> str: + """Assess how threatening an opponent is.""" + opp_vps = get_visible_victory_points(state, opponent_color) + my_vps = get_visible_victory_points(state, my_color) + + if opp_vps >= 8: + return "CRITICAL - Close to winning!" + elif opp_vps >= 6: + return "HIGH - Strong position" + elif opp_vps > my_vps: + return "MEDIUM - Ahead of you" + else: + return "LOW - Behind or equal" + + +def _analyze_expansion(game, my_color) -> Dict[str, Any]: + """Analyze expansion opportunities.""" + board = game.state.board + + buildable_nodes = list(board.buildable_node_ids(my_color)) + buildable_edges = [list(e) for e in board.buildable_edges(my_color)] + + # Assess each buildable node + node_assessments = [] + for node_id in buildable_nodes[:5]: # Limit to top 5 + # Get production at this node + tiles = board.map.adjacent_tiles.get(node_id, []) + resources = [] + total_prob = 0 + for tile in tiles: + if isinstance(tile, LandTile) and tile.resource: + from catanatron.models.map import number_probability + + prob = number_probability(tile.number) if tile.number else 0 + resources.append( + {"resource": tile.resource, "number": tile.number, "probability": prob} + ) + total_prob += prob + + node_assessments.append( + { + "node_id": node_id, + "resources": resources, + "total_production_probability": round(total_prob, 3), + } + ) + + # Sort by production + node_assessments.sort( + key=lambda x: x["total_production_probability"], reverse=True + ) + + return { + "buildable_settlement_locations": len(buildable_nodes), + "buildable_road_locations": len(buildable_edges), + "best_settlement_spots": node_assessments, + "expansion_advice": ( + "Focus on high-probability numbers (6, 8, 5, 9) and resource diversity" + if buildable_nodes + else "No settlement spots available - build roads to expand" + ), + } + + +def _analyze_blocking(game, my_color) -> Dict[str, Any]: + """Analyze blocking opportunities.""" + state = game.state + board = state.board + + blocking_opportunities = [] + for color in state.colors: + if color == my_color: + continue + + # Find nodes that would block this opponent + their_buildable = list(board.buildable_node_ids(color, initial_build_phase=False)) + my_buildable = set(board.buildable_node_ids(my_color)) + + overlap = [n for n in their_buildable if n in my_buildable] + if overlap: + blocking_opportunities.append( + { + "opponent": color.value, + "blockable_nodes": overlap[:3], + "their_vps": get_visible_victory_points(state, color), + } + ) + + return { + "blocking_opportunities": blocking_opportunities, + "advice": ( + "Consider blocking the leading player's expansion" + if blocking_opportunities + else "No immediate blocking opportunities" + ), + } + + +def _analyze_ports(game, my_color) -> Dict[str, Any]: + """Analyze port access and trading.""" + board = game.state.board + my_ports = list(board.get_player_port_resources(my_color)) + + port_info = { + "current_ports": my_ports, + "has_3_to_1_port": None in my_ports, + "specialized_ports": [p for p in my_ports if p is not None], + } + + # Determine trading rates + rates = {"wood": 4, "brick": 4, "sheep": 4, "wheat": 4, "ore": 4} + if None in my_ports: + rates = {r: 3 for r in rates} + for port_resource in my_ports: + if port_resource: + rates[port_resource.lower()] = 2 + + port_info["trading_rates"] = rates + port_info["advice"] = ( + "Good port access - consider using maritime trades" + if len(my_ports) > 0 + else "No ports yet - consider building towards coastal settlements" + ) + + return port_info + + +def _analyze_robber(game, my_color) -> Dict[str, Any]: + """Analyze robber placement options.""" + state = game.state + board = state.board + + placements = [] + for coord, tile in board.map.tiles.items(): + # Skip non-land tiles (Water, Port) and desert tiles + if not isinstance(tile, LandTile) or tile.resource is None: + continue + if coord == board.robber_coordinate: + continue + + # Who would be affected? + affected = {} + for node_id in tile.nodes.values(): + building = board.buildings.get(node_id) + if building: + color, btype = building + if color != my_color: + if color.value not in affected: + affected[color.value] = 0 + affected[color.value] += 2 if btype == CITY else 1 + + if affected: + from catanatron.models.map import number_probability + + prob = number_probability(tile.number) if tile.number else 0 + placements.append( + { + "coordinate": coord, + "resource": tile.resource, + "number": tile.number, + "probability": prob, + "affected_players": affected, + "total_impact": sum(affected.values()) * prob, + } + ) + + # Sort by impact + placements.sort(key=lambda x: x["total_impact"], reverse=True) + + return { + "best_robber_placements": placements[:5], + "current_robber_location": board.robber_coordinate, + "advice": "Target the leading player's highest-production tile", + } diff --git a/catanatron/catanatron/players/llm_player.py b/catanatron/catanatron/players/llm_player.py new file mode 100644 index 00000000..86e34ff4 --- /dev/null +++ b/catanatron/catanatron/players/llm_player.py @@ -0,0 +1,221 @@ +""" +Concrete LLM-powered player implementations. + +Provides ready-to-use player classes that combine LLM decision making +with various strategy advisors (AlphaBeta, MCTS, Value Function). +""" + +from typing import Literal, Optional, List + +from catanatron.game import Game +from catanatron.models.player import Color +from catanatron.models.enums import Action + +from catanatron.players.llm.base import BaseLLMPlayer +from catanatron.players.minimax import AlphaBetaPlayer +from catanatron.players.mcts import MCTSPlayer +from catanatron.players.value import ValueFunctionPlayer + + +class PydanticAIPlayer(BaseLLMPlayer): + """ + Pure LLM player without a strategy advisor. + + Makes decisions entirely based on LLM reasoning using the available tools. + Good for testing pure LLM capabilities or when you want maximum flexibility. + + Example: + player = PydanticAIPlayer(Color.RED, model="anthropic:claude-sonnet-4-20250514") + """ + + def __init__( + self, + color: Color, + model: str = "anthropic:claude-sonnet-4-20250514", + output_mode: Literal["index", "structured"] = "index", + is_bot: bool = True, + ): + """ + Initialize a pure LLM player. + + Args: + color: Player color + model: LLM model string (e.g., "anthropic:claude-sonnet-4-20250514", "openai:gpt-4o") + output_mode: "index" for fast mode, "structured" for detailed logging + is_bot: Whether this is a bot player (always True for LLM) + """ + super().__init__(color, model=model, output_mode=output_mode, is_bot=is_bot) + + def _get_strategy_recommendation( + self, game: Game, playable_actions: List[Action] + ) -> tuple[Optional[Action], Optional[str]]: + """Pure LLM player has no strategy advisor.""" + return None, None + + +class LLMAlphaBetaPlayer(BaseLLMPlayer, AlphaBetaPlayer): + """ + LLM player with AlphaBeta search as strategy advisor. + + The AlphaBeta algorithm looks ahead multiple moves and provides + a recommendation, which the LLM can follow or override. + + This combines the tactical depth of tree search with the + strategic reasoning of an LLM. + + Example: + player = LLMAlphaBetaPlayer( + Color.RED, + model="anthropic:claude-sonnet-4-20250514", + depth=2, + prunning=True + ) + """ + + def __init__( + self, + color: Color, + model: str = "anthropic:claude-sonnet-4-20250514", + depth: int = 2, + prunning: bool = False, + output_mode: Literal["index", "structured"] = "index", + is_bot: bool = True, + ): + """ + Initialize an LLM player with AlphaBeta advisor. + + Args: + color: Player color + model: LLM model string + depth: Search depth for AlphaBeta (higher = slower but better recommendations) + prunning: Whether to use alpha-beta pruning + output_mode: Action output format + is_bot: Whether this is a bot player + """ + # Initialize AlphaBeta first (it will call Player.__init__) + AlphaBetaPlayer.__init__(self, color, depth=depth, prunning=prunning) + + # Then initialize LLM components + self.model = model + self.output_mode = output_mode + self.is_bot = is_bot + + # Import here to avoid circular imports + from catanatron.players.llm.history import ConversationHistoryManager + + self.history_manager = ConversationHistoryManager() + self.agent = self._create_agent() + + def __repr__(self) -> str: + base = AlphaBetaPlayer.__repr__(self) + return f"LLM{base}[{self.model}]" + + +class LLMMCTSPlayer(BaseLLMPlayer, MCTSPlayer): + """ + LLM player with Monte Carlo Tree Search as strategy advisor. + + MCTS runs simulations to estimate the value of each action, + providing a probabilistic recommendation to the LLM. + + Good for situations where deep tactical analysis is important. + + Example: + player = LLMMCTSPlayer( + Color.RED, + model="anthropic:claude-sonnet-4-20250514", + num_simulations=20 + ) + """ + + def __init__( + self, + color: Color, + model: str = "anthropic:claude-sonnet-4-20250514", + num_simulations: int = 10, + prunning: bool = False, + output_mode: Literal["index", "structured"] = "index", + is_bot: bool = True, + ): + """ + Initialize an LLM player with MCTS advisor. + + Args: + color: Player color + model: LLM model string + num_simulations: Number of MCTS simulations (higher = slower but better) + prunning: Whether to use action pruning + output_mode: Action output format + is_bot: Whether this is a bot player + """ + # Initialize MCTS first + MCTSPlayer.__init__(self, color, num_simulations=num_simulations, prunning=prunning) + + # Then initialize LLM components + self.model = model + self.output_mode = output_mode + self.is_bot = is_bot + + from catanatron.players.llm.history import ConversationHistoryManager + + self.history_manager = ConversationHistoryManager() + self.agent = self._create_agent() + + def __repr__(self) -> str: + base = MCTSPlayer.__repr__(self) + return f"LLM{base}[{self.model}]" + + +class LLMValuePlayer(BaseLLMPlayer, ValueFunctionPlayer): + """ + LLM player with heuristic value function as strategy advisor. + + The value function provides a fast, greedy recommendation based on + hand-crafted heuristics. Less computationally expensive than + AlphaBeta or MCTS. + + Good for faster games or when compute is limited. + + Example: + player = LLMValuePlayer(Color.RED, model="anthropic:claude-sonnet-4-20250514") + """ + + def __init__( + self, + color: Color, + model: str = "anthropic:claude-sonnet-4-20250514", + value_fn_builder_name: Optional[str] = None, + output_mode: Literal["index", "structured"] = "index", + is_bot: bool = True, + ): + """ + Initialize an LLM player with Value Function advisor. + + Args: + color: Player color + model: LLM model string + value_fn_builder_name: Which value function to use ("C" for contender, None for base) + output_mode: Action output format + is_bot: Whether this is a bot player + """ + # Initialize ValueFunctionPlayer first + ValueFunctionPlayer.__init__( + self, color, value_fn_builder_name=value_fn_builder_name, is_bot=is_bot + ) + + # Then initialize LLM components + self.model = model + self.output_mode = output_mode + + from catanatron.players.llm.history import ConversationHistoryManager + + self.history_manager = ConversationHistoryManager() + self.agent = self._create_agent() + + def __repr__(self) -> str: + base = ValueFunctionPlayer.__str__(self) + return f"LLM{base}[{self.model}]" + + +# Aliases for convenience +LLMPlayer = PydanticAIPlayer diff --git a/pyproject.toml b/pyproject.toml index ba06d1be..10862dd9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,7 +16,11 @@ classifiers = [ "Operating System :: OS Independent", ] -dependencies = ["networkx", "click", "rich"] +dependencies = [ + "networkx", + "click", + "rich", +] [project.optional-dependencies] gym = ["gymnasium<=0.29.1", "numpy", "pandas", "fastparquet"] @@ -28,6 +32,12 @@ web = [ "sqlalchemy", "psycopg2-binary", ] +llm = [ + "anthropic>=0.40.0", + "openai>=1.50.0", + "pydantic-ai>=1.41.0", + "logfire>=4.17.0", +] dev = [ "black", "pytest", @@ -46,3 +56,5 @@ catanatron-play = "catanatron.cli.play:simulate" [tool.setuptools.packages.find] where = ["catanatron"] + + diff --git a/tests/test_llm_player.py b/tests/test_llm_player.py new file mode 100644 index 00000000..771e8980 --- /dev/null +++ b/tests/test_llm_player.py @@ -0,0 +1,447 @@ +""" +Tests for LLM player implementations. + +These tests use mocked LLM responses to avoid requiring actual API keys. +""" + +import pytest +from unittest.mock import Mock, patch, MagicMock +from dataclasses import dataclass +from typing import List, Any + +from catanatron.game import Game +from catanatron.models.player import Color, RandomPlayer +from catanatron.models.enums import Action, ActionType, ActionPrompt +from catanatron.state import State + +from tests.utils import build_initial_placements, advance_to_play_turn + + +# Skip all tests if pydantic-ai is not available +pytest.importorskip("pydantic_ai") + + +# ============= Test Fixtures ============= + + +@pytest.fixture +def two_player_game(): + """Create a simple 2-player game for testing.""" + players = [RandomPlayer(Color.RED), RandomPlayer(Color.BLUE)] + game = Game(players, seed=42) + return game + + +@pytest.fixture +def game_after_initial_placement(two_player_game): + """Create a game that's past the initial placement phase.""" + game = two_player_game + build_initial_placements(game) + return game + + +@pytest.fixture +def game_at_play_turn(game_after_initial_placement): + """Create a game at PLAY_TURN phase after rolling.""" + game = game_after_initial_placement + advance_to_play_turn(game) + return game + + +# ============= Unit Tests for Components ============= + + +class TestConversationHistoryManager: + """Tests for the conversation history manager.""" + + def test_new_turn_detection(self): + from catanatron.players.llm.history import ConversationHistoryManager + + manager = ConversationHistoryManager() + + # First call should always be new turn + assert manager.is_new_turn(0) + + # After setting turn, same turn is not new + manager.set_turn(0) + assert not manager.is_new_turn(0) + + # Different turn is new + assert manager.is_new_turn(1) + + def test_clear_history(self): + from catanatron.players.llm.history import ConversationHistoryManager + + manager = ConversationHistoryManager() + manager.add_message({"role": "user", "content": "test"}) + assert manager.message_count == 1 + + manager.clear() + assert manager.message_count == 0 + + def test_update_messages(self): + from catanatron.players.llm.history import ConversationHistoryManager + + manager = ConversationHistoryManager() + new_messages = [ + {"role": "user", "content": "hello"}, + {"role": "assistant", "content": "hi"}, + ] + manager.update(new_messages) + assert manager.get_messages() == new_messages + + def test_trim_to_last_n(self): + from catanatron.players.llm.history import ConversationHistoryManager + + manager = ConversationHistoryManager() + for i in range(10): + manager.add_message({"content": f"message_{i}"}) + + manager.trim_to_last_n(3) + assert manager.message_count == 3 + assert manager.messages[-1]["content"] == "message_9" + + +class TestStateFormatter: + """Tests for the state formatter.""" + + def test_format_full_state(self, game_after_initial_placement): + from catanatron.players.llm.state_formatter import StateFormatter + + state = StateFormatter.format_full_state( + game_after_initial_placement, Color.RED + ) + + assert "my_color" in state + assert state["my_color"] == "RED" + assert "my_state" in state + assert "opponents" in state + assert "board" in state + assert "bank" in state + + def test_format_for_prompt(self, game_after_initial_placement): + from catanatron.players.llm.state_formatter import StateFormatter + + prompt = StateFormatter.format_for_prompt( + game_after_initial_placement, Color.RED + ) + + assert "RED" in prompt + assert "Victory Points" in prompt + assert "Resources" in prompt + + def test_format_action(self): + from catanatron.players.llm.state_formatter import StateFormatter + + action = Action(Color.RED, ActionType.BUILD_SETTLEMENT, 5) + formatted = StateFormatter.format_action(action, 0) + + assert formatted["index"] == 0 + assert formatted["type"] == "BUILD_SETTLEMENT" + assert "node 5" in formatted["description"] + + def test_format_roll_action(self): + from catanatron.players.llm.state_formatter import StateFormatter + + action = Action(Color.RED, ActionType.ROLL, None) + formatted = StateFormatter.format_action(action, 0) + + assert formatted["type"] == "ROLL" + assert "Roll" in formatted["description"] + + def test_format_strategy_insight(self): + from catanatron.players.llm.state_formatter import StateFormatter + + action = Action(Color.RED, ActionType.END_TURN, None) + insight = StateFormatter.format_strategy_insight( + action, "No better moves available", "TestAdvisor" + ) + + assert "TestAdvisor" in insight + assert "End your turn" in insight + assert "No better moves" in insight + + +class TestOutputTypes: + """Tests for output type models.""" + + def test_action_by_index(self): + from catanatron.players.llm.output_types import ActionByIndex + + output = ActionByIndex(action_index=3, reasoning="Best action") + assert output.action_index == 3 + assert output.reasoning == "Best action" + + def test_action_by_index_without_reasoning(self): + from catanatron.players.llm.output_types import ActionByIndex + + output = ActionByIndex(action_index=0) + assert output.action_index == 0 + assert output.reasoning is None + + def test_build_settlement_action(self): + from catanatron.players.llm.output_types import BuildSettlementAction + + output = BuildSettlementAction(node_id=42, reasoning="Good production") + assert output.action_type == "BUILD_SETTLEMENT" + assert output.node_id == 42 + + +class TestCatanDependencies: + """Tests for the dependencies dataclass.""" + + def test_dependencies_creation(self, game_after_initial_placement): + from catanatron.players.llm.base import CatanDependencies + + game = game_after_initial_placement + deps = CatanDependencies( + color=Color.RED, + game=game, + playable_actions=game.playable_actions, + strategy_recommendation=None, + strategy_reasoning=None, + turn_number=0, + is_my_turn=True, + ) + + assert deps.color == Color.RED + assert deps.game is game + assert len(deps.playable_actions) > 0 + + +# ============= Integration Tests with Mocked LLM ============= + + +class TestPydanticAIPlayerWithMock: + """Tests for PydanticAIPlayer with mocked LLM responses.""" + + def test_player_creation(self): + from catanatron.players.llm_player import PydanticAIPlayer + + # This will fail if pydantic-ai is not properly configured + # but we're testing structure, not actual LLM calls + with patch("catanatron.players.llm.base.Agent") as MockAgent: + mock_agent = MagicMock() + MockAgent.return_value = mock_agent + + player = PydanticAIPlayer(Color.RED, model="test:model") + assert player.color == Color.RED + assert player.model == "test:model" + + def test_decide_returns_valid_action(self, game_after_initial_placement): + from catanatron.players.llm_player import PydanticAIPlayer + from catanatron.players.llm.output_types import ActionByIndex + + game = game_after_initial_placement + + with patch("catanatron.players.llm.base.Agent") as MockAgent: + # Setup mock agent + mock_agent = MagicMock() + mock_result = MagicMock() + mock_result.data = ActionByIndex(action_index=0) + mock_result.all_messages.return_value = [] + mock_agent.run_sync.return_value = mock_result + MockAgent.return_value = mock_agent + + player = PydanticAIPlayer(Color.RED) + action = player.decide(game, game.playable_actions) + + # Should return the first action since we mocked index 0 + assert action == game.playable_actions[0] + + def test_decide_with_invalid_index_falls_back(self, game_after_initial_placement): + from catanatron.players.llm_player import PydanticAIPlayer + from catanatron.players.llm.output_types import ActionByIndex + + game = game_after_initial_placement + + with patch("catanatron.players.llm.base.Agent") as MockAgent: + mock_agent = MagicMock() + mock_result = MagicMock() + # Return invalid index + mock_result.data = ActionByIndex(action_index=9999) + mock_result.all_messages.return_value = [] + mock_agent.run_sync.return_value = mock_result + MockAgent.return_value = mock_agent + + player = PydanticAIPlayer(Color.RED) + action = player.decide(game, game.playable_actions) + + # Should fallback to first action + assert action == game.playable_actions[0] + + def test_decide_on_error_falls_back(self, game_after_initial_placement): + from catanatron.players.llm_player import PydanticAIPlayer + + game = game_after_initial_placement + + with patch("catanatron.players.llm.base.Agent") as MockAgent: + mock_agent = MagicMock() + # Simulate LLM error + mock_agent.run_sync.side_effect = Exception("API Error") + MockAgent.return_value = mock_agent + + player = PydanticAIPlayer(Color.RED) + action = player.decide(game, game.playable_actions) + + # Should fallback to first action on error + assert action == game.playable_actions[0] + + def test_history_cleared_on_new_turn(self, game_after_initial_placement): + from catanatron.players.llm_player import PydanticAIPlayer + from catanatron.players.llm.output_types import ActionByIndex + + game = game_after_initial_placement + + with patch("catanatron.players.llm.base.Agent") as MockAgent: + mock_agent = MagicMock() + mock_result = MagicMock() + mock_result.data = ActionByIndex(action_index=0) + mock_result.all_messages.return_value = [{"test": "message"}] + mock_agent.run_sync.return_value = mock_result + MockAgent.return_value = mock_agent + + player = PydanticAIPlayer(Color.RED) + + # First decide call + player.decide(game, game.playable_actions) + assert player.history_manager.message_count > 0 + + # Simulate turn change + game.state.num_turns = 999 + + # Second decide call should clear history + player.decide(game, game.playable_actions) + assert player.history_manager.current_turn == 999 + + +class TestLLMAlphaBetaPlayer: + """Tests for LLMAlphaBetaPlayer.""" + + def test_player_creation(self): + from catanatron.players.llm_player import LLMAlphaBetaPlayer + + with patch("catanatron.players.llm.base.Agent") as MockAgent: + MockAgent.return_value = MagicMock() + + player = LLMAlphaBetaPlayer( + Color.RED, model="test:model", depth=3, prunning=True + ) + assert player.color == Color.RED + assert player.depth == 3 + assert player.prunning == True + + def test_repr(self): + from catanatron.players.llm_player import LLMAlphaBetaPlayer + + with patch("catanatron.players.llm.base.Agent") as MockAgent: + MockAgent.return_value = MagicMock() + + player = LLMAlphaBetaPlayer(Color.RED, model="test:model") + repr_str = repr(player) + assert "LLM" in repr_str + assert "AlphaBeta" in repr_str + + +class TestLLMMCTSPlayer: + """Tests for LLMMCTSPlayer.""" + + def test_player_creation(self): + from catanatron.players.llm_player import LLMMCTSPlayer + + with patch("catanatron.players.llm.base.Agent") as MockAgent: + MockAgent.return_value = MagicMock() + + player = LLMMCTSPlayer( + Color.RED, model="test:model", num_simulations=20 + ) + assert player.color == Color.RED + assert player.num_simulations == 20 + + +class TestLLMValuePlayer: + """Tests for LLMValuePlayer.""" + + def test_player_creation(self): + from catanatron.players.llm_player import LLMValuePlayer + + with patch("catanatron.players.llm.base.Agent") as MockAgent: + MockAgent.return_value = MagicMock() + + player = LLMValuePlayer(Color.RED, model="test:model") + assert player.color == Color.RED + + +# ============= CLI Registration Tests ============= + + +class TestCLIRegistration: + """Tests for CLI player registration.""" + + def test_llm_players_registered(self): + from catanatron.cli.cli_players import CLI_PLAYERS + + codes = [p.code for p in CLI_PLAYERS] + assert "LLM" in codes + assert "LLMAB" in codes + assert "LLMM" in codes + assert "LLMV" in codes + + def test_factory_functions_exist(self): + from catanatron.cli.cli_players import ( + create_llm_player, + create_llm_alphabeta_player, + create_llm_mcts_player, + create_llm_value_player, + ) + + # Just verify the functions are importable + assert callable(create_llm_player) + assert callable(create_llm_alphabeta_player) + assert callable(create_llm_mcts_player) + assert callable(create_llm_value_player) + + +# ============= Tool Function Tests ============= + + +class TestTools: + """Tests for the LLM agent tools.""" + + def test_get_game_state_tool_structure(self, game_after_initial_placement): + """Test that get_game_state returns expected structure.""" + from catanatron.players.llm.state_formatter import StateFormatter + + state = StateFormatter.format_full_state( + game_after_initial_placement, Color.RED + ) + + # Verify structure + assert "turn" in state + assert "phase" in state + assert "my_state" in state + assert "opponents" in state + assert "board" in state + assert "bank" in state + + # Verify my_state has resources (since it's self) + assert "resources" in state["my_state"] + assert "development_cards" in state["my_state"] + + def test_format_action_for_different_types(self): + """Test action formatting for various action types.""" + from catanatron.players.llm.state_formatter import StateFormatter + + actions = [ + (Action(Color.RED, ActionType.ROLL, None), "Roll"), + (Action(Color.RED, ActionType.END_TURN, None), "End"), + (Action(Color.RED, ActionType.BUILD_SETTLEMENT, 5), "node 5"), + (Action(Color.RED, ActionType.BUILD_ROAD, (1, 2)), "nodes 1 and 2"), + (Action(Color.RED, ActionType.BUY_DEVELOPMENT_CARD, None), "Buy"), + ] + + for action, expected_text in actions: + formatted = StateFormatter.format_action(action, 0) + assert expected_text in formatted["description"], ( + f"Expected '{expected_text}' in description for {action.action_type}" + ) diff --git a/uv.lock b/uv.lock new file mode 100644 index 00000000..6b9d18c5 --- /dev/null +++ b/uv.lock @@ 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uv.lock | 11 ++++ 6 files changed, 145 insertions(+), 23 deletions(-) diff --git a/catanatron/catanatron/cli/play.py b/catanatron/catanatron/cli/play.py index 0d4fa4d7..029895af 100644 --- a/catanatron/catanatron/cli/play.py +++ b/catanatron/catanatron/cli/play.py @@ -4,6 +4,7 @@ from typing import Literal, Union import click +import uuid from rich.console import Console from rich.table import Table from rich.progress import Progress, BarColumn, TimeRemainingColumn @@ -258,6 +259,7 @@ def play_batch_core(num_games, players, game_config, accumulators=[]): "catanatron.play_game", game_number=game_num + 1, total_games=num_games, + game_uuid=uuid.uuid4(), map_type=game_config.catan_map, ): for player in players: diff --git a/catanatron/catanatron/players/llm/base.py b/catanatron/catanatron/players/llm/base.py index 4f825405..5db76b37 100644 --- a/catanatron/catanatron/players/llm/base.py +++ b/catanatron/catanatron/players/llm/base.py @@ -9,7 +9,7 @@ from dataclasses import dataclass from typing import Optional, List, Literal, Any -from pydantic_ai import Agent, RunContext +from pydantic_ai import Agent, RunContext, UsageLimits, ToolDefinition, ModelSettings from catanatron.game import Game from catanatron.models.player import Player, Color @@ -61,6 +61,7 @@ class CatanDependencies: - Balance between expansion and resource accumulation ## Decision Making +Every turn use the get_game_and_action_analysis tool exactly onceto get a comprehensive analysis of the game state and available actions. Use the available tools to analyze the game state before making decisions. Consider both immediate gains and long-term strategy. If a strategy advisor recommendation is provided, consider it but you may choose differently based on your analysis. @@ -88,6 +89,8 @@ def __init__( color: Color, model: str = "anthropic:claude-sonnet-4-20250514", output_mode: Literal["index", "structured"] = "index", + timeout: Optional[float] = 10.0, + tool_calls_limit: int = 10, **strategy_kwargs, ): """ @@ -97,6 +100,9 @@ def __init__( color: Player color model: PydanticAI model string (e.g., "anthropic:claude-sonnet-4-20250514", "openai:gpt-4o") output_mode: "index" for fast mode, "structured" for detailed action types + timeout: Timeout in seconds for LLM calls (default: 10.0) + tool_calls_limit: Overall tool call limit per decision (default: 10) + tool_call_limit_per_tool: Maximum calls per individual tool (default: 2) **strategy_kwargs: Arguments passed to parent strategy player (e.g., depth for AlphaBeta) """ # Initialize parent player(s) @@ -104,6 +110,8 @@ def __init__( self.model = model self.output_mode = output_mode + self.timeout = timeout + self.tool_calls_limit = tool_calls_limit self.history_manager = ConversationHistoryManager() # Create the agent - defer to allow subclasses to customize @@ -119,6 +127,7 @@ def _create_agent(self) -> Agent: deps_type=CatanDependencies, output_type=ActionByIndex, system_prompt=CATAN_SYSTEM_PROMPT, + tool_timeout=self.timeout, ) # Register all tools @@ -205,6 +214,13 @@ def _build_prompt(self, game: Game) -> str: return "\n".join(prompt_parts) + def _filter_out_tools_by_name( + ctx: RunContext[bool], tool_defs: list[ToolDefinition] + ) -> list[ToolDefinition] | None: + if ctx.deps: + return [tool_def for tool_def in tool_defs if tool_def.name != 'launch_potato'] + return tool_defs + def _resolve_action( self, output: ActionByIndex, playable_actions: List[Action] ) -> Action: @@ -258,12 +274,21 @@ def decide(self, game: Game, playable_actions: List[Action]) -> Action: == game.state.color_to_index[self.color], ) - # 4. Run agent with history + # 4. Run agent with history and timeout try: + # Configure model settings with timeout for the entire run + model_settings = None + if self.timeout is not None and self.timeout > 0: + model_settings = ModelSettings(timeout=self.timeout) + result = self.agent.run_sync( self._build_prompt(game), deps=deps, message_history=self.history_manager.get_messages(), + usage_limits=UsageLimits( + tool_calls_limit=self.tool_calls_limit, + ), + model_settings=model_settings, ) # 5. Update history @@ -274,8 +299,18 @@ def decide(self, game: Game, playable_actions: List[Action]) -> Action: logfire.info(f"LLM player {self.color} chose action {action.action_type}", turn_number=current_turn) return action + except TimeoutError as e: + # Timeout occurred - fall back to strategy recommendation + logfire.warning( + f"LLM player {self.color} timed out after {self.timeout}s", + turn_number=current_turn, + error=str(e) + ) + if recommendation is not None: + return recommendation + return playable_actions[0] except Exception as e: - # On any error, fall back to strategy recommendation or first action + # On any other error (including UsageLimitExceeded), fall back to strategy recommendation or first action logfire.error(f"LLM player {self.color} error: {e}", turn_number=current_turn) if recommendation is not None: return recommendation @@ -285,3 +320,27 @@ def reset_state(self): """Reset state between games.""" super().reset_state() self.history_manager.clear() + + def __getstate__(self): + """ + Custom pickle serialization. + + Exclude unpickleable objects (agent, history_manager) that contain + SSLContext, thread locks, and other non-serializable components. + """ + state = self.__dict__.copy() + # Remove unpickleable objects + state.pop('agent', None) + state.pop('history_manager', None) + return state + + def __setstate__(self, state): + """ + Custom pickle deserialization. + + Recreate unpickleable objects after deserialization. + """ + self.__dict__.update(state) + # Recreate the agent and history manager + self.history_manager = ConversationHistoryManager() + self.agent = self._create_agent() diff --git a/catanatron/catanatron/players/llm/tools.py b/catanatron/catanatron/players/llm/tools.py index d5ca40d3..53ff6b2c 100644 --- a/catanatron/catanatron/players/llm/tools.py +++ b/catanatron/catanatron/players/llm/tools.py @@ -43,28 +43,40 @@ def register_tools(agent: Agent) -> None: agent: The PydanticAI agent to register tools on """ - @agent.tool - def get_game_state(ctx: RunContext[CatanDependencies]) -> Dict[str, Any]: + @agent.tool() + def get_game_and_action_analysis(ctx: RunContext[CatanDependencies]) -> Dict[str, Any]: """ - Get comprehensive current game state including your resources, - buildings, opponents, and board information. - - Use this to understand the overall game situation before making decisions. + Combine the analysis of the board into a single analysis. """ - return StateFormatter.format_full_state(ctx.deps.game, ctx.deps.color) - @agent.tool - def get_available_actions(ctx: RunContext[CatanDependencies]) -> List[Dict[str, Any]]: - """ - Get list of all valid actions you can take right now. + full_analysis = { + "game_state": StateFormatter.format_full_state(ctx.deps.game, ctx.deps.color), + "available_actions": [StateFormatter.format_action(action, i) for i, action in enumerate(ctx.deps.playable_actions)], + } + return full_analysis - Each action has an index, type, and description. - Return the index of your chosen action. - """ - actions = [] - for i, action in enumerate(ctx.deps.playable_actions): - actions.append(StateFormatter.format_action(action, i)) - return actions + # @agent.tool + # def get_game_state(ctx: RunContext[CatanDependencies]) -> Dict[str, Any]: + # """ + # Get comprehensive current game state including your resources, + # buildings, opponents, and board information. + + # Use this to understand the overall game situation before making decisions. + # """ + # return StateFormatter.format_full_state(ctx.deps.game, ctx.deps.color) + + # @agent.tool + # def get_available_actions(ctx: RunContext[CatanDependencies]) -> List[Dict[str, Any]]: + # """ + # Get list of all valid actions you can take right now. + + # Each action has an index, type, and description. + # Return the index of your chosen action. + # """ + # actions = [] + # for i, action in enumerate(ctx.deps.playable_actions): + # actions.append(StateFormatter.format_action(action, i)) + # return actions # @agent.tool # def get_strategy_recommendation(ctx: RunContext[CatanDependencies]) -> Dict[str, Any]: diff --git a/catanatron/catanatron/web/models.py b/catanatron/catanatron/web/models.py index 76f5d5f5..297cb571 100644 --- a/catanatron/catanatron/web/models.py +++ b/catanatron/catanatron/web/models.py @@ -30,7 +30,32 @@ class GameState(Base): @staticmethod def from_game(game: Game): state = json.dumps(game, cls=GameEncoder) - pickle_data = pickle.dumps(game, pickle.HIGHEST_PROTOCOL) + # Use game.copy() to create a clean copy + game_copy = game.copy() + + # Try standard pickle first (fastest and most compatible) + try: + pickle_data = pickle.dumps(game_copy, pickle.HIGHEST_PROTOCOL) + except (TypeError, AttributeError) as e: + # If standard pickle fails, try dill as fallback + # This handles more complex objects but is slower + try: + import dill + pickle_data = dill.dumps(game_copy) + except ImportError: + raise ValueError( + "Unable to pickle game. Standard pickle failed, and 'dill' is not installed. " + "Install dill for better serialization: pip install dill" + ) from e + except Exception as dill_error: + # If even dill fails, provide helpful error message + raise ValueError( + f"Unable to pickle game. Original error: {str(e)}. " + f"Dill also failed: {str(dill_error)}. " + "This may be due to unpickleable objects in player classes. " + "Ensure player classes implement __getstate__ and __setstate__ if needed." + ) from e + return GameState( uuid=game.id, state_index=get_state_index(game.state), @@ -91,5 +116,17 @@ def get_game_state(game_id, state_index=None) -> Game | None: if result is None: abort(404) db.session.commit() - game = pickle.loads(result.pickle_data) # type: ignore + # Try standard pickle first, fall back to dill if needed + try: + game = pickle.loads(result.pickle_data) # type: ignore + except (TypeError, pickle.UnpicklingError): + # If standard unpickler fails, try dill + try: + import dill + game = dill.loads(result.pickle_data) # type: ignore + except ImportError: + raise ValueError( + "Unable to unpickle game. " + "Install 'dill' package for better lock handling: pip install dill" + ) return game diff --git a/pyproject.toml b/pyproject.toml index 10862dd9..f06033fa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,6 +31,7 @@ web = [ "flask_sqlalchemy", "sqlalchemy", "psycopg2-binary", + "dill", ] llm = [ "anthropic>=0.40.0", diff --git a/uv.lock b/uv.lock index 6b9d18c5..2a4b5d02 100644 --- a/uv.lock +++ b/uv.lock @@ -355,6 +355,7 @@ llm = [ { name = "pydantic-ai" }, ] web = [ + { name = "dill" }, { name = "flask" }, { name = "flask-cors" }, { name = "flask-sqlalchemy" }, @@ -370,6 +371,7 @@ requires-dist = [ { name = "click" }, { name = "coverage", marker = "extra == 'dev'" }, { name = "coveralls", marker = "extra == 'dev'" }, + { name = "dill", marker = "extra == 'web'" }, { name = "fastparquet", marker = "extra == 'gym'" }, { name = "flask", marker = "extra == 'web'" }, { name = "flask-cors", marker = "extra == 'web'" }, @@ -864,6 +866,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/20/5b/0eceb9a5990de9025733a0d212ca43649ba9facd58b8552b6bf93c11439d/cyclopts-4.4.4-py3-none-any.whl", hash = "sha256:316f798fe2f2a30cb70e7140cfde2a46617bfbb575d31bbfdc0b2410a447bd83", size = 197398, upload-time = "2026-01-05T03:40:17.141Z" }, ] +[[package]] +name = "dill" +version = "0.4.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/12/80/630b4b88364e9a8c8c5797f4602d0f76ef820909ee32f0bacb9f90654042/dill-0.4.0.tar.gz", hash = "sha256:0633f1d2df477324f53a895b02c901fb961bdbf65a17122586ea7019292cbcf0", size = 186976, upload-time = "2025-04-16T00:41:48.867Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/50/3d/9373ad9c56321fdab5b41197068e1d8c25883b3fea29dd361f9b55116869/dill-0.4.0-py3-none-any.whl", hash = "sha256:44f54bf6412c2c8464c14e8243eb163690a9800dbe2c367330883b19c7561049", size = 119668, upload-time = "2025-04-16T00:41:47.671Z" }, +] + [[package]] name = "diskcache" version = "5.6.3" From dc207d17e3b994d6be9a87bb8b13414cf748b9ca Mon Sep 17 00:00:00 2001 From: Aditya Chivukula Date: Mon, 12 Jan 2026 20:24:29 +0100 Subject: [PATCH 4/8] LLMPlayer init bug fix, add GAME_ENGINE_SETUP.md --- GAME_ENGINE_SETUP.md | 1921 +++++++++++++++++++ catanatron/catanatron/players/llm_player.py | 6 + 2 files changed, 1927 insertions(+) create mode 100644 GAME_ENGINE_SETUP.md diff --git a/GAME_ENGINE_SETUP.md b/GAME_ENGINE_SETUP.md new file mode 100644 index 00000000..892f26f9 --- /dev/null +++ b/GAME_ENGINE_SETUP.md @@ -0,0 +1,1921 @@ +# Catanatron Game Engine Architecture + +## Table of Contents + +1. [Overview](#overview) +2. [Core Architecture](#core-architecture) +3. [Action System](#action-system) +4. [Game State Management](#game-state-management) +5. [Player Interface](#player-interface) +6. [Trade System Deep Dive](#trade-system-deep-dive) +7. [LLM Agent Integration](#llm-agent-integration) +8. [Implementation Guide](#implementation-guide) +9. [Code Examples](#code-examples) + +--- + +## Overview + +Catanatron is a fast, pure-Python implementation of the Settlers of Catan game engine designed for AI research and experimentation. The architecture follows a clean separation of concerns with an immutable action system and pluggable player implementations. + +### Key Design Principles + +- **Immutable Actions**: Actions are represented as namedtuples that describe intent +- **Redux-like Pattern**: State changes happen through explicit action handlers +- **Pluggable Players**: All player logic is encapsulated in Player subclasses +- **Deterministic Replay**: ActionRecords allow full game reconstruction +- **Performance**: Optimized for running thousands of simulations + +### High-Level Architecture + +```mermaid +flowchart TB + subgraph GameLayer [Game Layer] + Game[Game
Orchestration & Validation] + State[State
Game State Container] + Board[Board
Map & Buildings] + end + + subgraph ActionSystem [Action System] + ActionGen[generate_playable_actions
Move Generation] + ApplyAction[apply_action
State Mutation] + ActionTypes[ActionType Enum
16 Action Types] + end + + subgraph PlayerSystem [Player System] + Player[Player Base Class
decide method] + RandomP[RandomPlayer] + AlphaBeta[AlphaBetaPlayer] + Custom[Custom Players] + end + + Game --> State + State --> Board + Game --> ActionGen + ActionGen --> ActionTypes + Game --> ApplyAction + Player --> Game + + style Game fill:#e1f5ff + style State fill:#fff3e0 + style ActionGen fill:#f3e5f5 + style Player fill:#e8f5e9 +``` + +--- + +## Core Architecture + +### 1. Game Class (`catanatron/game.py`) + +The `Game` class is the main entry point and orchestrator for the game. It's a thin wrapper around the `State` class that manages the game loop and player interactions. + +#### Responsibilities + +- **Initialization**: Sets up the game with players, seed, map, and victory conditions +- **Game Loop**: Orchestrates turns through `play()` and `play_tick()` methods +- **Action Validation**: Validates actions before execution via `is_valid_action()` +- **Accumulator Hooks**: Provides lifecycle hooks for data collection +- **Winner Detection**: Determines when a player reaches victory points + +#### Key Attributes + +```python +class Game: + state: State # Current game state + playable_actions: List[Action] # Valid actions for current player + seed: int # Random seed for reproducibility + id: str # Unique game identifier + vps_to_win: int # Victory points needed (default: 10) +``` + +#### Game Loop Flow + +```mermaid +sequenceDiagram + participant Main + participant Game + participant Player + participant ActionGen as generate_playable_actions + participant ApplyAction as apply_action + participant State + + Main->>Game: play() + + loop Until Winner or Turn Limit + Game->>ActionGen: Generate moves + ActionGen->>State: Read state + ActionGen-->>Game: playable_actions + + Game->>Player: decide(game, playable_actions) + Player->>Game: Read game state + Player-->>Game: selected_action + + Game->>Game: is_valid_action() + + Game->>ApplyAction: apply_action(state, action) + ApplyAction->>State: Mutate state + ApplyAction-->>Game: ActionRecord + + Game->>ActionGen: Generate next moves + end + + Game-->>Main: winning_color +``` + +#### Game Loop Methods + +**`play(accumulators=[], decide_fn=None)`** + +Executes the entire game until completion or turn limit. + +- **accumulators**: List of `GameAccumulator` instances for data collection +- **decide_fn**: Optional function to override player decisions (for testing) +- **Returns**: Winning color or `None` if turn limit exceeded + +**`play_tick(decide_fn=None, accumulators=[])`** + +Advances the game by one ply (single player decision). + +- Calls current player's `decide()` method +- Executes the returned action +- Generates next set of playable actions +- **Returns**: `ActionRecord` of the executed action + +**`execute(action, validate_action=True, action_record=None)`** + +Internal method that executes a validated action. + +- Validates action if `validate_action=True` +- Calls `apply_action()` to mutate state +- Regenerates playable actions +- **Returns**: `ActionRecord` + +### 2. State Class (`catanatron/state.py`) + +The `State` class is the central data structure containing all game state information. It's designed to be efficiently copyable for tree search algorithms. + +#### Core State Components + +```python +class State: + # Players and turn management + players: List[Player] # DEPRECATED: Use colors instead + colors: Tuple[Color] # Seating order (immutable) + color_to_index: Dict[Color, int] # Color lookup cache + current_player_index: int # Who decides next + current_turn_index: int # Whose turn it is + num_turns: int # Completed turns counter + + # Board state + board: Board # Map, buildings, roads, robber + + # Player state (flat dictionary for performance) + player_state: Dict[str, Any] # Format: P{index}_{ATTRIBUTE} + + # Resources and cards + resource_freqdeck: List[int] # Bank's resources [W,B,S,W,O] + development_listdeck: List[FastDevCard] # Shuffled dev cards + + # Buildings cache (for performance) + buildings_by_color: Dict[Color, Dict[FastBuildingType, List]] + + # Action history + action_records: List[ActionRecord] # Full game log + + # Game phase indicators + current_prompt: ActionPrompt # Current game phase + is_initial_build_phase: bool + is_discarding: bool + is_moving_knight: bool + is_road_building: bool + free_roads_available: int + + # Trade state + is_resolving_trade: bool + current_trade: Tuple # (offer[5], ask[5], turn_idx) + acceptees: Tuple[bool] # Who accepted trade + + discard_limit: int # Cards before discarding (default: 7) +``` + +#### Player State Dictionary + +The `player_state` dictionary is a flat structure optimized for performance and feature extraction. Each player's state is prefixed with `P{index}_`. + +**Format Example**: For player at index 0 (P0): + +```python +{ + # Victory Points + "P0_VICTORY_POINTS": 2, # Public VPs (visible to all) + "P0_ACTUAL_VICTORY_POINTS": 3, # Includes hidden VP cards + + # Resources in hand (visible only to P0) + "P0_WOOD_IN_HAND": 2, + "P0_BRICK_IN_HAND": 1, + "P0_SHEEP_IN_HAND": 3, + "P0_WHEAT_IN_HAND": 1, + "P0_ORE_IN_HAND": 0, + + # Development cards + "P0_KNIGHT_IN_HAND": 1, + "P0_YEAR_OF_PLENTY_IN_HAND": 0, + "P0_MONOPOLY_IN_HAND": 0, + "P0_ROAD_BUILDING_IN_HAND": 1, + "P0_VICTORY_POINT_IN_HAND": 1, + + # Played development cards (visible to all) + "P0_PLAYED_KNIGHT": 2, + "P0_PLAYED_YEAR_OF_PLENTY": 0, + "P0_PLAYED_MONOPOLY": 0, + "P0_PLAYED_ROAD_BUILDING": 1, + + # Building inventory + "P0_ROADS_AVAILABLE": 13, + "P0_SETTLEMENTS_AVAILABLE": 3, + "P0_CITIES_AVAILABLE": 4, + + # Special achievements + "P0_HAS_ROAD": True, # Longest road + "P0_HAS_ARMY": False, # Largest army + "P0_LONGEST_ROAD_LENGTH": 7, + + # Turn state + "P0_HAS_ROLLED": True, + "P0_HAS_PLAYED_DEVELOPMENT_CARD_IN_TURN": False, + + # Dev card playability tracking + "P0_KNIGHT_OWNED_AT_START": True, # Can play this turn + "P0_MONOPOLY_OWNED_AT_START": False, + "P0_YEAR_OF_PLENTY_OWNED_AT_START": False, + "P0_ROAD_BUILDING_OWNED_AT_START": True, +} +``` + +#### State Copying + +The `State.copy()` method creates a deep copy for simulations: + +```python +def copy(self) -> State: + """Creates independent copy for tree search without affecting original""" + state_copy = State([], None, initialize=False) + state_copy.players = self.players # Reference (immutable) + state_copy.board = self.board.copy() + state_copy.player_state = self.player_state.copy() + state_copy.resource_freqdeck = self.resource_freqdeck.copy() + # ... copies all mutable structures + return state_copy +``` + +### 3. Board Class (`catanatron/models/board.py`) + +The `Board` class manages the physical game board: tiles, nodes (settlement locations), edges (road locations), and ports. + +#### Key Components + +```python +class Board: + map: CatanMap # Tile layout and topology + buildings: Dict[int, Tuple] # node_id -> (Color, BuildingType) + roads: Dict[Tuple, Color] # (node1, node2) -> Color + robber_coordinate: Coordinate # Current robber location +``` + +#### Board Operations + +- **`buildable_node_ids(color, initial_build_phase=False)`**: Returns valid settlement locations +- **`buildable_edges(color)`**: Returns valid road locations +- **`build_settlement(color, node_id, is_free)`**: Places settlement, returns road impact +- **`build_city(color, node_id)`**: Upgrades settlement to city +- **`build_road(color, edge)`**: Places road, calculates longest road +- **`get_player_port_resources(color)`**: Returns accessible port types + +--- + +## Action System + +The action system is built on immutable data structures and explicit action types. All actions flow through a single `apply_action()` function that routes to specific handlers. + +### Action Representation + +```python +from collections import namedtuple + +Action = namedtuple("Action", ["color", "action_type", "value"]) +ActionRecord = namedtuple("ActionRecord", ["action", "result"]) +``` + +**Action Fields**: +- **color**: `Color` enum of the player taking the action +- **action_type**: `ActionType` enum indicating the action category +- **value**: Polymorphic parameter specific to action_type + +**ActionRecord Fields**: +- **action**: The `Action` that was executed +- **result**: Result of non-deterministic actions (dice rolls, cards drawn, etc.) + +### ActionType Enum + +There are **16 action types** divided into categories: + +```python +class ActionType(Enum): + # Turn management + ROLL = "ROLL" # value: None + END_TURN = "END_TURN" # value: None + DISCARD = "DISCARD" # value: None (random) or List[Resource] + MOVE_ROBBER = "MOVE_ROBBER" # value: (coordinate, Color|None) + + # Building + BUILD_ROAD = "BUILD_ROAD" # value: (node_id, node_id) + BUILD_SETTLEMENT = "BUILD_SETTLEMENT" # value: node_id + BUILD_CITY = "BUILD_CITY" # value: node_id + BUY_DEVELOPMENT_CARD = "BUY_DEVELOPMENT_CARD" # value: None + + # Development cards + PLAY_KNIGHT_CARD = "PLAY_KNIGHT_CARD" # value: None + PLAY_YEAR_OF_PLENTY = "PLAY_YEAR_OF_PLENTY" # value: (Resource, Resource) + PLAY_MONOPOLY = "PLAY_MONOPOLY" # value: Resource + PLAY_ROAD_BUILDING = "PLAY_ROAD_BUILDING" # value: None + + # Trade + MARITIME_TRADE = "MARITIME_TRADE" # value: (R,R,R,R,R_asked) or with None + OFFER_TRADE = "OFFER_TRADE" # value: (offer[5], ask[5]) + ACCEPT_TRADE = "ACCEPT_TRADE" # value: (offer[5], ask[5], turn_idx) + REJECT_TRADE = "REJECT_TRADE" # value: (offer[5], ask[5], turn_idx) + CONFIRM_TRADE = "CONFIRM_TRADE" # value: (offer[5], ask[5], Color) + CANCEL_TRADE = "CANCEL_TRADE" # value: None +``` + +### ActionPrompt Enum + +The `ActionPrompt` indicates what type of decision is expected from the current player: + +```python +class ActionPrompt(Enum): + BUILD_INITIAL_SETTLEMENT = "BUILD_INITIAL_SETTLEMENT" + BUILD_INITIAL_ROAD = "BUILD_INITIAL_ROAD" + PLAY_TURN = "PLAY_TURN" # Normal turn actions + DISCARD = "DISCARD" # Must discard half + MOVE_ROBBER = "MOVE_ROBBER" # After 7 or knight + DECIDE_TRADE = "DECIDE_TRADE" # Accept/reject trade offer + DECIDE_ACCEPTEES = "DECIDE_ACCEPTEES" # Choose who to trade with +``` + +### Action Generation (`catanatron/models/actions.py`) + +The `generate_playable_actions(state)` function is called after every state change to produce the list of valid actions for the current player. + +#### Generation Logic by Prompt + +```python +def generate_playable_actions(state: State) -> List[Action]: + action_prompt = state.current_prompt + color = state.current_color() + + if action_prompt == ActionPrompt.BUILD_INITIAL_SETTLEMENT: + return settlement_possibilities(state, color, True) + + elif action_prompt == ActionPrompt.BUILD_INITIAL_ROAD: + return initial_road_possibilities(state, color) + + elif action_prompt == ActionPrompt.MOVE_ROBBER: + return robber_possibilities(state, color) + + elif action_prompt == ActionPrompt.PLAY_TURN: + if state.is_road_building: + return road_building_possibilities(state, color, False) + + actions = [] + + # Dev cards (playable before and after rolling) + if player_can_play_dev(state, color, "YEAR_OF_PLENTY"): + actions.extend(year_of_plenty_possibilities(color, state.resource_freqdeck)) + if player_can_play_dev(state, color, "MONOPOLY"): + actions.extend(monopoly_possibilities(color)) + if player_can_play_dev(state, color, "KNIGHT"): + actions.append(Action(color, ActionType.PLAY_KNIGHT_CARD, None)) + if player_can_play_dev(state, color, "ROAD_BUILDING"): + actions.append(Action(color, ActionType.PLAY_ROAD_BUILDING, None)) + + if not player_has_rolled(state, color): + actions.append(Action(color, ActionType.ROLL, None)) + else: + # After rolling + actions.append(Action(color, ActionType.END_TURN, None)) + actions.extend(road_building_possibilities(state, color)) + actions.extend(settlement_possibilities(state, color)) + actions.extend(city_possibilities(state, color)) + + if player_can_afford_dev_card(state, color): + actions.append(Action(color, ActionType.BUY_DEVELOPMENT_CARD, None)) + + actions.extend(maritime_trade_possibilities(state, color)) + + return actions + + elif action_prompt == ActionPrompt.DISCARD: + return discard_possibilities(color) + + elif action_prompt == ActionPrompt.DECIDE_TRADE: + actions = [Action(color, ActionType.REJECT_TRADE, state.current_trade)] + freqdeck = get_player_freqdeck(state, color) + asked = state.current_trade[5:10] + if freqdeck_contains(freqdeck, asked): + actions.append(Action(color, ActionType.ACCEPT_TRADE, state.current_trade)) + return actions + + elif action_prompt == ActionPrompt.DECIDE_ACCEPTEES: + actions = [Action(color, ActionType.CANCEL_TRADE, None)] + for other_color, accepted in zip(state.colors, state.acceptees): + if accepted: + actions.append(Action( + color, ActionType.CONFIRM_TRADE, + (*state.current_trade[:10], other_color) + )) + return actions +``` + +### Action Application (`catanatron/apply_action.py`) + +The `apply_action()` function is the single entry point for state mutations. It follows a Redux-like pattern: + +```python +def apply_action(state: State, action: Action, action_record: ActionRecord = None) -> ActionRecord: + """ + Main controller that routes actions to appropriate handlers. + + Responsibilities: + - Mutate state according to action + - Update current_player_index and current_prompt + - Handle non-deterministic results (dice, card draws) + - Append ActionRecord to state.action_records + + Returns: + ActionRecord with action and result (if non-deterministic) + """ + if action.action_type == ActionType.END_TURN: + action_record = apply_end_turn(state, action) + elif action.action_type == ActionType.BUILD_SETTLEMENT: + action_record = apply_build_settlement(state, action) + # ... routes to 16 different handlers + + state.action_records.append(action_record) + return action_record +``` + +#### Action Handler Pattern + +Each handler follows this pattern: + +```python +def apply_{action_name}(state: State, action: Action, action_record=None): + # 1. Validate preconditions (optional, done in is_valid_action) + # 2. Handle non-deterministic results + # 3. Mutate state + # 4. Update current_player_index if needed + # 5. Update current_prompt if needed + # 6. Return ActionRecord +``` + +#### Example: Rolling Dice + +```python +def apply_roll(state: State, action: Action, action_record=None): + key = player_key(state, action.color) + state.player_state[f"{key}_HAS_ROLLED"] = True + + # Use provided dice (for replay) or roll new ones + dices = action_record.result if action_record else roll_dice() + number = dices[0] + dices[1] + action = Action(action.color, action.action_type, dices) + + if number == 7: + # Check if anyone needs to discard + discarders = [ + player_num_resource_cards(state, color) > state.discard_limit + for color in state.colors + ] + + if any(discarders): + state.current_player_index = discarders.index(True) + state.current_prompt = ActionPrompt.DISCARD + state.is_discarding = True + else: + state.current_prompt = ActionPrompt.MOVE_ROBBER + state.is_moving_knight = True + else: + # Yield resources + payout, _ = yield_resources(state.board, state.resource_freqdeck, number) + for color, resource_freqdeck in payout.items(): + player_freqdeck_add(state, color, resource_freqdeck) + state.resource_freqdeck = freqdeck_subtract( + state.resource_freqdeck, resource_freqdeck + ) + state.current_prompt = ActionPrompt.PLAY_TURN + + return ActionRecord(action=action, result=dices) +``` + +--- + +## Game State Management + +### Turn Management + +The game uses two separate indices to handle out-of-turn actions: + +- **`current_turn_index`**: Which player's turn it is (for resource collection, etc.) +- **`current_player_index`**: Which player needs to make a decision right now + +These can differ during: +- Discarding phase (after rolling 7) +- Trade negotiation (other players accept/reject) +- Robber stealing (moving robber affects multiple players) + +### Phase Transitions + +```mermaid +stateDiagram-v2 + [*] --> InitialBuild: Game Start + + InitialBuild --> InitialBuild: Place Settlement+Road + InitialBuild --> PlayTurn: All Initial Placements Done + + PlayTurn --> PlayTurn: Play Dev Card + PlayTurn --> Roll: Roll Dice + + Roll --> Discard: Rolled 7 & Players > 7 cards + Roll --> MoveRobber: Rolled 7 & No Discards + Roll --> PlayTurn: Not 7, Resources Yielded + + Discard --> Discard: More Players to Discard + Discard --> MoveRobber: All Discarded + + MoveRobber --> PlayTurn: Robber Moved + + PlayTurn --> PlayTurn: Build/Trade/Buy + PlayTurn --> EndTurn: End Turn + + EndTurn --> PlayTurn: Next Player's Turn + EndTurn --> [*]: Winner Declared +``` + +### Frequency Decks (freqdeck) + +Resources and some card types are represented as frequency arrays for performance: + +```python +# Format: [WOOD, BRICK, SHEEP, WHEAT, ORE] +freqdeck = [2, 1, 3, 1, 0] # 2 wood, 1 brick, 3 sheep, 1 wheat, 0 ore + +# Helper functions +freqdeck_add(deck, other_deck) # Add resources +freqdeck_subtract(deck, other_deck) # Remove resources +freqdeck_contains(deck, other_deck) # Can afford? +freqdeck_count(deck, resource) # Count of resource +``` + +### Building Costs + +```python +from catanatron.models.decks import ( + ROAD_COST_FREQDECK, # [1, 1, 0, 0, 0] - Wood + Brick + SETTLEMENT_COST_FREQDECK, # [1, 1, 1, 1, 0] - W + B + S + Wh + CITY_COST_FREQDECK, # [0, 0, 0, 2, 3] - 2 Wheat + 3 Ore + DEVELOPMENT_CARD_COST_FREQDECK, # [0, 0, 1, 1, 1] - S + Wh + O +) +``` + +--- + +## Player Interface + +### Base Player Class + +All player implementations must extend the `Player` base class: + +```python +class Player: + """Interface for player decision logic""" + + def __init__(self, color: Color, is_bot: bool = True): + self.color = color + self.is_bot = is_bot + + def decide(self, game: Game, playable_actions: List[Action]) -> Action: + """ + Called when this player needs to make a decision. + + Args: + game: Complete game state (read-only) + playable_actions: List of valid actions right now + + Returns: + One Action from playable_actions, OR a valid OFFER_TRADE action + + Note: + OFFER_TRADE can be returned during PLAY_TURN after rolling, + even if not in playable_actions (special case). + """ + raise NotImplementedError + + def reset_state(self): + """Hook for resetting state between games""" + pass +``` + +### Built-in Player Types + +#### 1. RandomPlayer + +Selects randomly from playable actions: + +```python +class RandomPlayer(Player): + def decide(self, game, playable_actions): + return random.choice(playable_actions) +``` + +#### 2. SimplePlayer + +Always takes first action (useful for testing): + +```python +class SimplePlayer(Player): + def decide(self, game, playable_actions): + return playable_actions[0] +``` + +#### 3. AlphaBetaPlayer + +Implements minimax with alpha-beta pruning: + +```python +class AlphaBetaPlayer(Player): + def __init__(self, color, depth=2, prunning=False, value_fn_builder_name=None): + super().__init__(color) + self.depth = depth + self.prunning = prunning + + def decide(self, game, playable_actions): + if len(playable_actions) == 1: + return playable_actions[0] + + game_copy = game.copy() + best_action, best_value = self.alphabeta( + game_copy, self.depth, float("-inf"), float("inf"), deadline, node + ) + return best_action or playable_actions[0] +``` + +### Accessing Game State + +Players can access full game state through the `game` parameter: + +```python +def decide(self, game: Game, playable_actions: List[Action]) -> Action: + # Access state + state = game.state + + # Current player info + my_color = self.color + my_key = player_key(state, my_color) + my_resources = get_player_freqdeck(state, my_color) + my_vps = state.player_state[f"{my_key}_ACTUAL_VICTORY_POINTS"] + + # Board info + board = state.board + my_settlements = state.buildings_by_color[my_color][SETTLEMENT] + my_cities = state.buildings_by_color[my_color][CITY] + + # Other players + for other_color in state.colors: + if other_color == my_color: + continue + other_key = player_key(state, other_color) + other_vps = state.player_state[f"{other_key}_VICTORY_POINTS"] + other_num_cards = player_num_resource_cards(state, other_color) + + # Game phase + current_prompt = state.current_prompt + has_rolled = player_has_rolled(state, my_color) + + return choose_best_action(playable_actions) +``` + +### Feature Extraction + +The `features.py` module provides utilities to convert game state to vectors: + +```python +from catanatron.features import create_sample, create_sample_vector + +def decide(self, game, playable_actions): + # Get feature dictionary + features = create_sample(game, self.color) + # features = { + # "P0_ACTUAL_VPS": 3, + # "P0_WOOD_IN_HAND": 2, + # "BANK_WOOD": 15, + # "NODE0_P0_SETTLEMENT": True, + # ... + # } + + # Or get as numpy array + feature_names = get_feature_ordering(num_players=len(game.state.colors)) + vector = create_sample_vector(game, self.color, feature_names) + + return neural_network_decision(vector, playable_actions) +``` + +--- + +## Trade System Deep Dive + +### Maritime Trade (Bank Trading) + +Maritime trades are player-to-bank exchanges at fixed rates. + +#### Trade Rates + +- **4:1**: Default rate (no port) +- **3:1**: Three-to-one port (any resource) +- **2:1**: Specialized port (specific resource only) + +#### Maritime Trade Action Format + +```python +# value is 5-tuple: (give, give, give, give, get) +# Nones indicate unused slots (for port trades) + +# 4:1 Example: Trade 4 wood for 1 ore +Action(color, ActionType.MARITIME_TRADE, (WOOD, WOOD, WOOD, WOOD, ORE)) + +# 3:1 Example: Trade 3 sheep for 1 brick +Action(color, ActionType.MARITIME_TRADE, (SHEEP, SHEEP, SHEEP, None, BRICK)) + +# 2:1 Example: Trade 2 wheat for 1 wood (wheat port) +Action(color, ActionType.MARITIME_TRADE, (WHEAT, WHEAT, None, None, WOOD)) +``` + +#### Generation Logic + +```python +def maritime_trade_possibilities(state, color) -> List[Action]: + """Generates all valid maritime trades""" + hand_freqdeck = get_player_freqdeck(state, color) + port_resources = state.board.get_player_port_resources(color) + # port_resources might contain None (3:1) or specific resources (2:1) + + trade_offers = set() + + # Determine rates per resource + rates = {WOOD: 4, BRICK: 4, SHEEP: 4, WHEAT: 4, ORE: 4} + if None in port_resources: + rates = {WOOD: 3, BRICK: 3, SHEEP: 3, WHEAT: 3, ORE: 3} + for resource in port_resources: + if resource is not None: + rates[resource] = 2 + + # Generate trades + for resource_out in RESOURCES: + amount_owned = hand_freqdeck[RESOURCES.index(resource_out)] + if amount_owned >= rates[resource_out]: + resource_out_list = [resource_out] * rates[resource_out] + resource_out_list += [None] * (4 - rates[resource_out]) + + for resource_in in RESOURCES: + if resource_in != resource_out: + if bank_has_resource(state, resource_in): + trade_offer = tuple(resource_out_list + [resource_in]) + trade_offers.add(trade_offer) + + return [Action(color, ActionType.MARITIME_TRADE, t) for t in trade_offers] +``` + +### Domestic Trade (Player-to-Player) + +Domestic trading is more complex, involving multiple phases and player decisions. + +#### Trade State Machine + +```mermaid +stateDiagram-v2 + [*] --> PLAY_TURN: Player's normal turn + + PLAY_TURN --> OFFER_TRADE: Player offers trade + note right of OFFER_TRADE: Store offer in state.current_trade + + OFFER_TRADE --> DECIDE_TRADE_P1: Ask Player 1 + DECIDE_TRADE_P1 --> DECIDE_TRADE_P2: ACCEPT or REJECT + DECIDE_TRADE_P2 --> DECIDE_TRADE_P3: ACCEPT or REJECT + + DECIDE_TRADE_P3 --> PLAY_TURN: All rejected + note left of PLAY_TURN: No one accepted + + DECIDE_TRADE_P3 --> DECIDE_ACCEPTEES: At least one accepted + note right of DECIDE_ACCEPTEES: Original player chooses + + DECIDE_ACCEPTEES --> PLAY_TURN: CANCEL_TRADE + DECIDE_ACCEPTEES --> CONFIRM_TRADE: CONFIRM with specific player + CONFIRM_TRADE --> PLAY_TURN: Trade executed +``` + +#### Trade Action Values + +All domestic trade actions use a 10-tuple format: + +```python +# Format: (offered_freqdeck[5], asked_freqdeck[5]) +# Example: Offer 2 wood + 1 sheep for 1 wheat + 1 ore +trade_value = ( + 2, 0, 1, 0, 0, # Offering: 2 wood, 0 brick, 1 sheep, 0 wheat, 0 ore + 0, 0, 0, 1, 1 # Asking: 0 wood, 0 brick, 0 sheep, 1 wheat, 1 ore +) +``` + +#### Trade Validation + +```python +def is_valid_trade(action_value): + """Validates trade offer structure""" + offering = action_value[:5] + asking = action_value[5:] + + # Can't give away resources for free + if sum(offering) == 0 or sum(asking) == 0: + return False + + # Can't trade same resource both ways + for i, j in zip(offering, asking): + if i > 0 and j > 0: + return False + + return True +``` + +#### Phase-by-Phase Breakdown + +**1. OFFER_TRADE (Proposer's Turn)** + +```python +# Player creates offer during PLAY_TURN (after rolling) +offering = [2, 0, 0, 0, 0] # 2 wood +asking = [0, 1, 0, 0, 0] # 1 brick +action = Action(my_color, ActionType.OFFER_TRADE, tuple(offering + asking)) + +# State changes: +# - is_resolving_trade = True +# - current_trade = (*action.value, current_turn_index) +# - current_player_index = first other player +# - current_prompt = ActionPrompt.DECIDE_TRADE +``` + +**2. DECIDE_TRADE (Other Players Respond)** + +Each other player is prompted in seating order: + +```python +# Generated actions for responder: +actions = [ + Action(responder_color, ActionType.REJECT_TRADE, current_trade), +] + +# Can only accept if has the resources +if has_resources(responder, current_trade[5:10]): + actions.append( + Action(responder_color, ActionType.ACCEPT_TRADE, current_trade) + ) +``` + +After each response: +- If ACCEPT: Add to `state.acceptees` tuple +- Move to next player +- If no more players: goto DECIDE_ACCEPTEES or back to PLAY_TURN + +**3. DECIDE_ACCEPTEES (Proposer Chooses)** + +If at least one player accepted: + +```python +# Generated actions for proposer: +actions = [Action(proposer_color, ActionType.CANCEL_TRADE, None)] + +for other_color, accepted in zip(state.colors, state.acceptees): + if accepted: + actions.append(Action( + proposer_color, + ActionType.CONFIRM_TRADE, + (*state.current_trade[:10], other_color) + )) +``` + +**4. CONFIRM_TRADE (Execute Trade)** + +```python +def apply_confirm_trade(state, action): + offering = action.value[:5] + asking = action.value[5:10] + trading_partner = action.value[10] + + # Transfer resources + player_freqdeck_subtract(state, action.color, offering) + player_freqdeck_add(state, action.color, asking) + player_freqdeck_subtract(state, trading_partner, asking) + player_freqdeck_add(state, trading_partner, offering) + + # Reset trade state + state.is_resolving_trade = False + state.current_trade = (0,0,0,0,0,0,0,0,0,0,0) + state.acceptees = tuple(False for _ in state.colors) + + # Back to original player's turn + state.current_player_index = state.current_turn_index + state.current_prompt = ActionPrompt.PLAY_TURN +``` + +#### Current Limitations + +**No Negotiation**: The current system has NO negotiation mechanism: +- Trade offers are immutable once created +- No counter-offers +- No communication between players +- No reasoning or persuasion +- Accept/reject is purely based on game state + +**For LLM integration, you will need to add**: +1. A negotiation phase before OFFER_TRADE +2. Message passing between players +3. History tracking +4. Natural language understanding/generation + +--- + +## LLM Agent Integration + +### Integration Challenges + +Integrating LLM agents with Catanatron requires addressing several challenges: + +1. **State Representation**: Converting game state to LLM-readable format +2. **Action Space**: Mapping between natural language and Action objects +3. **Negotiation**: Adding communication capabilities to the trade system +4. **Tool Calling**: Exposing game actions as LLM tools +5. **Multi-Agent Coordination**: Managing multiple LLM agents in one game +6. **Performance**: LLM API latency vs game simulation speed + +### Architecture Options + +#### Option A: LLM Player Wrapper (Recommended for MVP) + +Wrap the Player class with LLM API calls: + +```mermaid +flowchart LR + subgraph CatanatronEngine [Catanatron Engine] + Game[Game] + State[State] + end + + subgraph LLMPlayer [LLM Player Agent] + StateFormatter[State Formatter] + LLMClient[LLM API Client] + ActionParser[Action Parser] + end + + Game -->|"decide(game, playable_actions)"| StateFormatter + StateFormatter -->|"JSON + NL prompt"| LLMClient + LLMClient -->|"Tool call response"| ActionParser + ActionParser -->|"Action object"| Game + + style LLMPlayer fill:#e8f5e9 +``` + +**Advantages**: +- Minimal engine modifications +- Clean separation of concerns +- Easy to swap LLM providers +- Can mix LLM and traditional AI players + +**Disadvantages**: +- Negotiation requires new action types +- Each decision is isolated (no conversation state) +- Performance bottleneck at LLM API + +#### Option B: Negotiation-First Architecture (Recommended for Full System) + +Add a negotiation layer to the engine: + +```mermaid +flowchart TB + subgraph GameEngine [Game Engine with Negotiation] + State[State + negotiation_history] + NegPhase[Negotiation Phase] + TradePhase[Trade Execution Phase] + end + + subgraph LLMAgents [LLM Agents] + Agent1[Player 1 LLM] + Agent2[Player 2 LLM] + Agent3[Player 3 LLM] + Agent4[Player 4 LLM] + end + + State --> NegPhase + NegPhase -->|Messages| Agent1 + NegPhase -->|Messages| Agent2 + NegPhase -->|Messages| Agent3 + NegPhase -->|Messages| Agent4 + + Agent1 -->|SEND_MESSAGE| NegPhase + Agent2 -->|SEND_MESSAGE| NegPhase + Agent3 -->|END_NEGOTIATION| TradePhase + + TradePhase -->|OFFER_TRADE| State + + style GameEngine fill:#fff3e0 + style LLMAgents fill:#e1f5ff +``` + +### Recommended Hybrid Approach + +For all-LLM games with natural language negotiation, use a hybrid: + +#### 1. LLM Player Base Class + +```python +import anthropic # or openai +from typing import List, Dict, Any +import json + +class LLMPlayer(Player): + """Base class for LLM-powered players""" + + def __init__(self, color: Color, api_key: str, model: str = "claude-3-5-sonnet-20241022"): + super().__init__(color, is_bot=True) + self.client = anthropic.Anthropic(api_key=api_key) + self.model = model + self.conversation_history = [] + + def decide(self, game: Game, playable_actions: List[Action]) -> Action: + """ + Make decision using LLM with tool calling. + """ + # Format game state + state_json = self.format_state_json(game) + state_summary = self.format_state_natural_language(game) + + # Build system prompt + system_prompt = self.build_system_prompt() + + # Build user message + user_message = f""" + {state_summary} + + Current game state (detailed): + ```json + {json.dumps(state_json, indent=2)} + ``` + + You must now make a decision. Use the available tools to choose your action. + """ + + # Define tools + tools = self.create_action_tools(playable_actions) + + # Call LLM + response = self.client.messages.create( + model=self.model, + max_tokens=4096, + system=system_prompt, + messages=[ + *self.conversation_history, + {"role": "user", "content": user_message} + ], + tools=tools + ) + + # Parse response + action = self.parse_llm_response(response, playable_actions) + + # Update conversation history + self.conversation_history.append({"role": "user", "content": user_message}) + self.conversation_history.append({"role": "assistant", "content": response.content}) + + return action + + def format_state_json(self, game: Game) -> Dict[str, Any]: + """Convert game state to structured JSON""" + state = game.state + my_key = player_key(state, self.color) + + return { + "my_color": self.color.value, + "turn_number": state.num_turns, + "my_resources": { + "wood": state.player_state[f"{my_key}_WOOD_IN_HAND"], + "brick": state.player_state[f"{my_key}_BRICK_IN_HAND"], + "sheep": state.player_state[f"{my_key}_SHEEP_IN_HAND"], + "wheat": state.player_state[f"{my_key}_WHEAT_IN_HAND"], + "ore": state.player_state[f"{my_key}_ORE_IN_HAND"], + }, + "my_development_cards": { + "knight": state.player_state[f"{my_key}_KNIGHT_IN_HAND"], + "year_of_plenty": state.player_state[f"{my_key}_YEAR_OF_PLENTY_IN_HAND"], + "monopoly": state.player_state[f"{my_key}_MONOPOLY_IN_HAND"], + "road_building": state.player_state[f"{my_key}_ROAD_BUILDING_IN_HAND"], + "victory_point": state.player_state[f"{my_key}_VICTORY_POINT_IN_HAND"], + }, + "my_victory_points": state.player_state[f"{my_key}_ACTUAL_VICTORY_POINTS"], + "my_buildings": { + "settlements": state.buildings_by_color[self.color][SETTLEMENT], + "cities": state.buildings_by_color[self.color][CITY], + "roads": state.buildings_by_color[self.color][ROAD], + }, + "opponents": self.format_opponents_info(game), + "board": self.format_board_info(game), + "phase": state.current_prompt.value, + "has_rolled": state.player_state[f"{my_key}_HAS_ROLLED"], + } + + def format_state_natural_language(self, game: Game) -> str: + """Create human-readable game state summary""" + state = game.state + my_key = player_key(state, self.color) + + summary = f"=== CATAN GAME STATE ===\n\n" + summary += f"You are playing as {self.color.value}\n" + summary += f"Turn: {state.num_turns}\n\n" + + # Your state + summary += f"YOUR STATE:\n" + summary += f"- Victory Points: {state.player_state[f'{my_key}_ACTUAL_VICTORY_POINTS']}\n" + summary += f"- Resources: " + summary += f"Wood({state.player_state[f'{my_key}_WOOD_IN_HAND']}), " + summary += f"Brick({state.player_state[f'{my_key}_BRICK_IN_HAND']}), " + summary += f"Sheep({state.player_state[f'{my_key}_SHEEP_IN_HAND']}), " + summary += f"Wheat({state.player_state[f'{my_key}_WHEAT_IN_HAND']}), " + summary += f"Ore({state.player_state[f'{my_key}_ORE_IN_HAND']})\n" + + # Opponents + summary += f"\nOPPONENTS:\n" + for color in state.colors: + if color == self.color: + continue + key = player_key(state, color) + summary += f"- {color.value}: " + summary += f"{state.player_state[f'{key}_VICTORY_POINTS']} VPs, " + summary += f"{player_num_resource_cards(state, color)} cards\n" + + # Current phase + if state.current_prompt == ActionPrompt.PLAY_TURN: + if not state.player_state[f"{my_key}_HAS_ROLLED"]: + summary += f"\nPHASE: You need to ROLL the dice\n" + else: + summary += f"\nPHASE: Your turn - you can build, trade, or end turn\n" + else: + summary += f"\nPHASE: {state.current_prompt.value}\n" + + return summary + + def create_action_tools(self, playable_actions: List[Action]) -> List[Dict]: + """Convert playable actions to LLM tool definitions""" + tools = [] + + # Group actions by type + action_groups = {} + for action in playable_actions: + action_type = action.action_type + if action_type not in action_groups: + action_groups[action_type] = [] + action_groups[action_type].append(action) + + # Create tool for each action type + for action_type, actions in action_groups.items(): + if action_type == ActionType.ROLL: + tools.append({ + "name": "roll_dice", + "description": "Roll the dice to generate resources", + "input_schema": { + "type": "object", + "properties": {}, + "required": [] + } + }) + elif action_type == ActionType.BUILD_SETTLEMENT: + tools.append({ + "name": "build_settlement", + "description": "Build a settlement at a specific location", + "input_schema": { + "type": "object", + "properties": { + "node_id": { + "type": "integer", + "description": f"Node ID where to build. Valid options: {[a.value for a in actions]}", + "enum": [a.value for a in actions] + } + }, + "required": ["node_id"] + } + }) + elif action_type == ActionType.BUILD_ROAD: + tools.append({ + "name": "build_road", + "description": "Build a road between two nodes", + "input_schema": { + "type": "object", + "properties": { + "edge": { + "type": "array", + "description": "Edge as [node1, node2]", + "items": {"type": "integer"} + } + }, + "required": ["edge"] + } + }) + elif action_type == ActionType.END_TURN: + tools.append({ + "name": "end_turn", + "description": "End your turn", + "input_schema": { + "type": "object", + "properties": {}, + "required": [] + } + }) + # Add more action types... + + return tools + + def parse_llm_response(self, response, playable_actions: List[Action]) -> Action: + """Parse LLM tool call response into Action""" + # Extract tool call from response + for block in response.content: + if block.type == "tool_use": + tool_name = block.name + tool_input = block.input + + # Map tool call to action + if tool_name == "roll_dice": + return next(a for a in playable_actions + if a.action_type == ActionType.ROLL) + elif tool_name == "build_settlement": + node_id = tool_input["node_id"] + return next(a for a in playable_actions + if a.action_type == ActionType.BUILD_SETTLEMENT + and a.value == node_id) + elif tool_name == "end_turn": + return next(a for a in playable_actions + if a.action_type == ActionType.END_TURN) + # Add more mappings... + + # Fallback to random if parsing fails + return random.choice(playable_actions) + + def build_system_prompt(self) -> str: + """Build system prompt with game rules and strategy""" + return """You are an expert Settlers of Catan player. Your goal is to reach 10 victory points before your opponents. + +Key rules: +- Victory points come from: settlements (1), cities (2), longest road (2), largest army (2), victory point cards (1) +- You need resources to build: settlements (wood+brick+sheep+wheat), cities (2 wheat+3 ore), roads (wood+brick) +- You can trade with other players or the bank +- Development cards cost sheep+wheat+ore + +Strategy tips: +- Diversify resource production by building on different numbers +- Secure important intersection spots early +- Build towards valuable port locations +- Consider blocking opponents' expansion +- Time development card plays strategically + +Make decisions by calling the appropriate tool.""" + + def reset_state(self): + """Reset conversation history between games""" + self.conversation_history = [] +``` + +#### 2. Adding Negotiation Support + +To add natural language negotiation to the trade system, we need to extend the engine: + +**New State Fields**: + +```python +class State: + # ... existing fields ... + + # Negotiation support + is_negotiating: bool = False + negotiation_initiator: Optional[Color] = None + negotiation_participants: List[Color] = [] + negotiation_messages: List[Dict] = [] # [{"from": Color, "to": List[Color], "text": str}] +``` + +**New Action Types**: + +```python +class ActionType(Enum): + # ... existing types ... + + # Negotiation + INITIATE_NEGOTIATION = "INITIATE_NEGOTIATION" # value: List[Color] participants + SEND_MESSAGE = "SEND_MESSAGE" # value: {"to": List[Color], "text": str} + END_NEGOTIATION = "END_NEGOTIATION" # value: None (proceeds to OFFER_TRADE) +``` + +**New Action Prompt**: + +```python +class ActionPrompt(Enum): + # ... existing prompts ... + NEGOTIATING = "NEGOTIATING" +``` + +**Negotiation Flow**: + +```mermaid +sequenceDiagram + participant P1 as Player 1 LLM + participant Game + participant P2 as Player 2 LLM + participant P3 as Player 3 LLM + + P1->>Game: INITIATE_NEGOTIATION([P2, P3]) + Game->>Game: is_negotiating=True + + Game->>P1: Your turn in negotiation + P1->>Game: SEND_MESSAGE("I need wheat, offering wood") + + Game->>P2: Your turn in negotiation + P2->>Game: SEND_MESSAGE("I have wheat, need ore") + + Game->>P3: Your turn in negotiation + P3->>Game: SEND_MESSAGE("I can give ore for brick") + + Game->>P1: Your turn in negotiation + P1->>Game: END_NEGOTIATION + + Game->>P1: Make OFFER_TRADE + P1->>Game: OFFER_TRADE([2 wood], [1 wheat]) + + Game->>P2: DECIDE_TRADE + P2->>Game: ACCEPT_TRADE + + Game->>P3: DECIDE_TRADE + P3->>Game: REJECT_TRADE + + Game->>P1: DECIDE_ACCEPTEES + P1->>Game: CONFIRM_TRADE(P2) +``` + +**Implementation**: + +```python +# In apply_action.py + +def apply_initiate_negotiation(state: State, action: Action): + """Start negotiation phase""" + participants = action.value # List[Color] + + state.is_negotiating = True + state.negotiation_initiator = action.color + state.negotiation_participants = [action.color] + participants + state.negotiation_messages = [] + + # Stay with current player for first message + state.current_prompt = ActionPrompt.NEGOTIATING + return ActionRecord(action=action, result=None) + +def apply_send_message(state: State, action: Action): + """Send message during negotiation""" + message_data = action.value # {"to": List[Color], "text": str} + + # Add to history + state.negotiation_messages.append({ + "from": action.color, + "to": message_data["to"], + "text": message_data["text"], + "timestamp": len(state.negotiation_messages) + }) + + # Pass to next participant in negotiation + current_idx = state.negotiation_participants.index(action.color) + next_idx = (current_idx + 1) % len(state.negotiation_participants) + + next_color = state.negotiation_participants[next_idx] + state.current_player_index = state.colors.index(next_color) + + return ActionRecord(action=action, result=None) + +def apply_end_negotiation(state: State, action: Action): + """End negotiation, return to normal turn""" + state.is_negotiating = False + state.current_prompt = ActionPrompt.PLAY_TURN + state.current_player_index = state.current_turn_index + + # Keep negotiation history for context + return ActionRecord(action=action, result=None) + +# In models/actions.py + +def generate_playable_actions(state: State) -> List[Action]: + # ... existing code ... + + elif action_prompt == ActionPrompt.NEGOTIATING: + color = state.current_color() + participants = [c for c in state.negotiation_participants if c != color] + + return [ + # Send message to all participants + Action(color, ActionType.SEND_MESSAGE, { + "to": participants, + "text": "" # LLM will fill this + }), + # Or end negotiation and make offer + Action(color, ActionType.END_NEGOTIATION, None) + ] + + elif action_prompt == ActionPrompt.PLAY_TURN: + # ... existing code ... + + # Add negotiation option (after rolling) + if player_has_rolled(state, color): + # Can initiate negotiation with any subset of other players + other_players = [c for c in state.colors if c != color] + # For simplicity, offer negotiation with all others + actions.append(Action( + color, + ActionType.INITIATE_NEGOTIATION, + other_players + )) +``` + +**LLMPlayer Integration with Negotiation**: + +```python +class LLMPlayer(Player): + # ... previous code ... + + def decide(self, game: Game, playable_actions: List[Action]) -> Action: + state = game.state + + # Special handling for negotiation + if state.current_prompt == ActionPrompt.NEGOTIATING: + return self.decide_negotiation(game, playable_actions) + + # Normal decision logic + return self.decide_normal(game, playable_actions) + + def decide_negotiation(self, game: Game, playable_actions: List[Action]) -> Action: + """Handle negotiation phase with natural language""" + state = game.state + + # Format conversation history + conversation_summary = "\n".join([ + f"{msg['from'].value}: {msg['text']}" + for msg in state.negotiation_messages + ]) + + user_message = f""" + You are in a trade negotiation. Here's the conversation so far: + + {conversation_summary} + + Current game state: + {self.format_state_natural_language(game)} + + You can either: + 1. Send a message to continue negotiating + 2. End negotiation and make a trade offer + + What do you want to do? + """ + + # Define tools for negotiation + tools = [ + { + "name": "send_message", + "description": "Send a message to other players in the negotiation", + "input_schema": { + "type": "object", + "properties": { + "message": { + "type": "string", + "description": "Your message to other players" + } + }, + "required": ["message"] + } + }, + { + "name": "end_negotiation", + "description": "End the negotiation and make a trade offer", + "input_schema": { + "type": "object", + "properties": {}, + "required": [] + } + } + ] + + response = self.client.messages.create( + model=self.model, + max_tokens=4096, + system=self.build_system_prompt(), + messages=[{"role": "user", "content": user_message}], + tools=tools + ) + + # Parse response + for block in response.content: + if block.type == "tool_use": + if block.name == "send_message": + message_text = block.input["message"] + # Find SEND_MESSAGE action and fill in text + for action in playable_actions: + if action.action_type == ActionType.SEND_MESSAGE: + return Action( + action.color, + action.action_type, + {**action.value, "text": message_text} + ) + elif block.name == "end_negotiation": + return next(a for a in playable_actions + if a.action_type == ActionType.END_NEGOTIATION) + + # Fallback + return playable_actions[0] +``` + +### Complete Integration Example + +Here's a complete example of running a game with LLM players: + +```python +from catanatron import Game +from catanatron.models.player import Color +from llm_player import LLMPlayer # Your implementation + +# Create LLM players +players = [ + LLMPlayer(Color.RED, api_key="your-key", model="claude-3-5-sonnet-20241022"), + LLMPlayer(Color.BLUE, api_key="your-key"), + LLMPlayer(Color.ORANGE, api_key="your-key"), + LLMPlayer(Color.WHITE, api_key="your-key"), +] + +# Create and play game +game = Game(players, seed=42) +winner = game.play() + +print(f"Winner: {winner}") +print(f"Total turns: {game.state.num_turns}") + +# Review negotiation history +for action_record in game.state.action_records: + if action_record.action.action_type == ActionType.SEND_MESSAGE: + msg = action_record.action.value + print(f"{action_record.action.color.value}: {msg['text']}") +``` + +--- + +## Implementation Guide + +### Step 1: Set Up Environment + +```bash +# Clone Catanatron +git clone https://github.com/bcollazo/catanatron +cd catanatron + +# Install dependencies +pip install -e catanatron/ + +# Install LLM SDK +pip install anthropic # or openai +``` + +### Step 2: Create Basic LLMPlayer + +Start with a simple version without negotiation: + +```python +# llm_player_simple.py + +from catanatron.models.player import Player, Color +from catanatron.models.enums import Action, ActionType +import anthropic +import random + +class SimpleLLMPlayer(Player): + def __init__(self, color: Color, api_key: str): + super().__init__(color) + self.client = anthropic.Anthropic(api_key=api_key) + + def decide(self, game, playable_actions): + # For now, just pick randomly + # Later: Add LLM decision making + return random.choice(playable_actions) +``` + +### Step 3: Add State Formatting + +```python +def format_state(self, game): + """Convert game state to readable format""" + state = game.state + my_key = f"P{state.color_to_index[self.color]}" + + return { + "my_resources": { + "wood": state.player_state[f"{my_key}_WOOD_IN_HAND"], + "brick": state.player_state[f"{my_key}_BRICK_IN_HAND"], + "sheep": state.player_state[f"{my_key}_SHEEP_IN_HAND"], + "wheat": state.player_state[f"{my_key}_WHEAT_IN_HAND"], + "ore": state.player_state[f"{my_key}_ORE_IN_HAND"], + }, + "victory_points": state.player_state[f"{my_key}_ACTUAL_VICTORY_POINTS"], + # Add more fields... + } +``` + +### Step 4: Implement LLM Decision Making + +```python +def decide(self, game, playable_actions): + state_info = self.format_state(game) + + # Create prompt + prompt = f"""You are playing Settlers of Catan as {self.color.value}. + +Current state: {state_info} + +Available actions: +{self.format_actions(playable_actions)} + +Choose the best action.""" + + # Call LLM + response = self.client.messages.create( + model="claude-3-5-sonnet-20241022", + max_tokens=1024, + messages=[{"role": "user", "content": prompt}] + ) + + # Parse response and map to action + return self.parse_action(response.content[0].text, playable_actions) +``` + +### Step 5: Add Negotiation Support + +Follow the architecture described in the "Adding Negotiation Support" section above. + +### Step 6: Test and Iterate + +```python +# test_llm_game.py + +from catanatron import Game +from llm_player import LLMPlayer +from catanatron.models.player import Color, RandomPlayer + +# Start with mixed game (LLM vs Random) +players = [ + LLMPlayer(Color.RED, api_key="your-key"), + RandomPlayer(Color.BLUE), + RandomPlayer(Color.ORANGE), + RandomPlayer(Color.WHITE), +] + +game = Game(players, seed=42) +winner = game.play() + +print(f"Winner: {winner}") +``` + +--- + +## Code Examples + +### Example 1: Reading Game State + +```python +from catanatron import Game +from catanatron.models.player import Color, RandomPlayer +from catanatron.state_functions import player_key, get_player_freqdeck, player_num_resource_cards + +# Create game +players = [RandomPlayer(c) for c in [Color.RED, Color.BLUE]] +game = Game(players) + +# Play until after initial placement +while game.state.is_initial_build_phase: + game.play_tick() + +# Read state +state = game.state +my_color = Color.RED +my_key = player_key(state, my_color) + +print(f"My VPs: {state.player_state[f'{my_key}_ACTUAL_VICTORY_POINTS']}") +print(f"My resources: {get_player_freqdeck(state, my_color)}") +print(f"Total cards: {player_num_resource_cards(state, my_color)}") +print(f"Current prompt: {state.current_prompt}") +``` + +### Example 2: Creating Custom Actions + +```python +from catanatron.models.enums import Action, ActionType, WOOD, BRICK, WHEAT + +# Build settlement at node 5 +settlement_action = Action(Color.RED, ActionType.BUILD_SETTLEMENT, 5) + +# Build road between nodes 3 and 5 +road_action = Action(Color.RED, ActionType.BUILD_ROAD, (3, 5)) + +# Offer trade: 2 wood for 1 wheat +trade_action = Action( + Color.RED, + ActionType.OFFER_TRADE, + (2, 0, 0, 0, 0, # Offering: 2 wood + 0, 0, 0, 1, 0) # Asking: 1 wheat +) + +# Maritime trade: 4 brick for 1 wheat (4:1 rate) +maritime_action = Action( + Color.RED, + ActionType.MARITIME_TRADE, + (BRICK, BRICK, BRICK, BRICK, WHEAT) +) +``` + +### Example 3: Simulating Future States + +```python +def evaluate_action(game, action): + """Evaluate an action by simulating it""" + game_copy = game.copy() + game_copy.execute(action) + + # Get value of resulting state + state = game_copy.state + my_key = player_key(state, game.state.current_color()) + vps = state.player_state[f'{my_key}_ACTUAL_VICTORY_POINTS'] + + return vps + +# Evaluate all possible actions +for action in game.playable_actions: + value = evaluate_action(game, action) + print(f"{action.action_type}: value = {value}") +``` + +### Example 4: Running Batch Simulations + +```python +from catanatron import Game +from catanatron.models.player import Color, RandomPlayer + +def run_simulations(num_games=100): + """Run multiple games and collect statistics""" + players = [RandomPlayer(c) for c in [Color.RED, Color.BLUE]] + wins = {Color.RED: 0, Color.BLUE: 0} + + for i in range(num_games): + game = Game(players, seed=i) + winner = game.play() + if winner: + wins[winner] += 1 + + print(f"Results after {num_games} games:") + print(f" RED: {wins[Color.RED]} wins") + print(f" BLUE: {wins[Color.BLUE]} wins") + +run_simulations(100) +``` + +### Example 5: Game Accumulator for Data Collection + +```python +from catanatron.game import GameAccumulator + +class TradeLogger(GameAccumulator): + """Log all trade attempts""" + + def __init__(self): + self.trades = [] + + def step(self, game, action): + if action.action_type in [ + ActionType.OFFER_TRADE, + ActionType.ACCEPT_TRADE, + ActionType.REJECT_TRADE, + ActionType.CONFIRM_TRADE, + ]: + self.trades.append({ + "turn": game.state.num_turns, + "action": action, + "offerer": action.color, + }) + + def after(self, game): + print(f"Game finished with {len(self.trades)} trade actions") + +# Use accumulator +logger = TradeLogger() +game = Game(players) +game.play(accumulators=[logger]) + +# Analyze trades +for trade in logger.trades: + print(f"Turn {trade['turn']}: {trade['action']}") +``` + +--- + +## Key Takeaways + +### Architecture Summary + +1. **Game Loop**: `Game` class orchestrates turn-by-turn play +2. **State Management**: `State` class holds all game state in a flat dictionary +3. **Action System**: Immutable `Action` namedtuples describe player intent +4. **Action Handlers**: Redux-like `apply_action()` routes to specific mutators +5. **Move Generation**: `generate_playable_actions()` produces valid actions +6. **Player Interface**: All AI logic in `Player.decide()` method + +### For LLM Integration + +**Recommended Approach**: +- Create `LLMPlayer(Player)` subclass +- Format state as JSON + natural language +- Use LLM tool calling for action selection +- Add negotiation layer to engine for multi-agent chat +- Track conversation history in state + +**Key Implementation Points**: +1. State representation: Use `create_sample()` + custom formatters +2. Action mapping: Convert between tools and Action objects +3. Negotiation: Add SEND_MESSAGE action type +4. Multi-agent: Each player has separate LLM context +5. Performance: Cache state representations, use async APIs + +### Next Steps + +1. Implement `LLMPlayer` base class with state formatting +2. Test with single LLM against random players +3. Add negotiation action types to enums.py +4. Implement negotiation handlers in apply_action.py +5. Update action generation for negotiation phase +6. Test full multi-LLM game with negotiation +7. Optimize for performance (caching, batching) + +--- + +## Appendix + +### File Reference + +| File | Path | Purpose | +|------|------|---------| +| Game | `catanatron/game.py` | Main game loop and orchestration | +| State | `catanatron/state.py` | Game state container | +| Action Application | `catanatron/apply_action.py` | Redux-like action handlers | +| Action Generation | `catanatron/models/actions.py` | Move generation logic | +| Enums | `catanatron/models/enums.py` | ActionType, ActionPrompt, etc. | +| Player | `catanatron/models/player.py` | Player base class | +| Board | `catanatron/models/board.py` | Map and buildings | +| State Functions | `catanatron/state_functions.py` | State reading/mutation helpers | +| Features | `catanatron/features.py` | State-to-vector conversion | +| Gym Environment | `catanatron/gym/envs/catanatron_env.py` | OpenAI Gym wrapper | + +### Resource Links + +- **Catanatron Repository**: https://github.com/bcollazo/catanatron +- **Documentation**: Linked from repository README +- **Catan Rules**: https://www.catan.com/understand-catan/game-rules +- **Anthropic API Docs**: https://docs.anthropic.com/ +- **OpenAI API Docs**: https://platform.openai.com/docs + +### Glossary + +- **Action**: Immutable namedtuple describing player intent +- **ActionRecord**: Action + result (for non-deterministic actions) +- **ActionPrompt**: Game phase indicator (PLAY_TURN, DECIDE_TRADE, etc.) +- **Freqdeck**: Array representation of resources [WOOD, BRICK, SHEEP, WHEAT, ORE] +- **Node**: Settlement/city location (intersection of tiles) +- **Edge**: Road location (between two nodes) +- **VPs**: Victory Points +- **Accumulator**: Hook for data collection during game + +--- + +*This documentation is designed to help you integrate LLM agents with natural language negotiation into the Catanatron game engine. For questions or contributions, refer to the main repository.* diff --git a/catanatron/catanatron/players/llm_player.py b/catanatron/catanatron/players/llm_player.py index 86e34ff4..4e59e22b 100644 --- a/catanatron/catanatron/players/llm_player.py +++ b/catanatron/catanatron/players/llm_player.py @@ -78,6 +78,8 @@ def __init__( model: str = "anthropic:claude-sonnet-4-20250514", depth: int = 2, prunning: bool = False, + timeout: Optional[float] = 10.0, + tool_calls_limit: int = 10, output_mode: Literal["index", "structured"] = "index", is_bot: bool = True, ): @@ -89,6 +91,8 @@ def __init__( model: LLM model string depth: Search depth for AlphaBeta (higher = slower but better recommendations) prunning: Whether to use alpha-beta pruning + timeout: Timeout in seconds for LLM calls (default: 10.0) + tool_calls_limit: Overall tool call limit per decision (default: 10) output_mode: Action output format is_bot: Whether this is a bot player """ @@ -97,6 +101,8 @@ def __init__( # Then initialize LLM components self.model = model + self.timeout = timeout + self.tool_calls_limit = tool_calls_limit self.output_mode = output_mode self.is_bot = is_bot From 248ac8e437529f549557c54602467f7a5daa44d5 Mon Sep 17 00:00:00 2001 From: Aditya Chivukula Date: Tue, 13 Jan 2026 00:50:38 +0100 Subject: [PATCH 5/8] bug fix --- catanatron/catanatron/players/llm/base.py | 18 +++-- catanatron/catanatron/players/llm/tools.py | 2 + catanatron/catanatron/players/llm_player.py | 76 ++++++++++----------- 3 files changed, 51 insertions(+), 45 deletions(-) diff --git a/catanatron/catanatron/players/llm/base.py b/catanatron/catanatron/players/llm/base.py index 5db76b37..5e50ee2b 100644 --- a/catanatron/catanatron/players/llm/base.py +++ b/catanatron/catanatron/players/llm/base.py @@ -61,7 +61,7 @@ class CatanDependencies: - Balance between expansion and resource accumulation ## Decision Making -Every turn use the get_game_and_action_analysis tool exactly onceto get a comprehensive analysis of the game state and available actions. +Every turn use the `get_game_and_action_analysis` tool IMMEDIATELY before doing any thinking or reasoning. Do this exactly once per turn to get a comprehensive analysis of the game state and available actions. Use the available tools to analyze the game state before making decisions. Consider both immediate gains and long-term strategy. If a strategy advisor recommendation is provided, consider it but you may choose differently based on your analysis. @@ -89,8 +89,9 @@ def __init__( color: Color, model: str = "anthropic:claude-sonnet-4-20250514", output_mode: Literal["index", "structured"] = "index", - timeout: Optional[float] = 10.0, + timeout: Optional[float] = 120.0, tool_calls_limit: int = 10, + is_bot: bool = True, **strategy_kwargs, ): """ @@ -100,13 +101,18 @@ def __init__( color: Player color model: PydanticAI model string (e.g., "anthropic:claude-sonnet-4-20250514", "openai:gpt-4o") output_mode: "index" for fast mode, "structured" for detailed action types - timeout: Timeout in seconds for LLM calls (default: 10.0) + timeout: Timeout in seconds for LLM calls (default: 120.0) tool_calls_limit: Overall tool call limit per decision (default: 10) - tool_call_limit_per_tool: Maximum calls per individual tool (default: 2) + is_bot: Whether this is a bot player (default: True) **strategy_kwargs: Arguments passed to parent strategy player (e.g., depth for AlphaBeta) """ # Initialize parent player(s) + # Note: is_bot is handled explicitly here to avoid passing it through + # strategy_kwargs to strategy players that don't accept it super().__init__(color, **strategy_kwargs) + + # Set is_bot after parent chain init (Player sets it, strategy players may not) + self.is_bot = is_bot self.model = model self.output_mode = output_mode @@ -306,12 +312,10 @@ def decide(self, game: Game, playable_actions: List[Action]) -> Action: turn_number=current_turn, error=str(e) ) - if recommendation is not None: - return recommendation - return playable_actions[0] except Exception as e: # On any other error (including UsageLimitExceeded), fall back to strategy recommendation or first action logfire.error(f"LLM player {self.color} error: {e}", turn_number=current_turn) + finally: if recommendation is not None: return recommendation return playable_actions[0] diff --git a/catanatron/catanatron/players/llm/tools.py b/catanatron/catanatron/players/llm/tools.py index 53ff6b2c..842b1232 100644 --- a/catanatron/catanatron/players/llm/tools.py +++ b/catanatron/catanatron/players/llm/tools.py @@ -52,6 +52,8 @@ def get_game_and_action_analysis(ctx: RunContext[CatanDependencies]) -> Dict[str full_analysis = { "game_state": StateFormatter.format_full_state(ctx.deps.game, ctx.deps.color), "available_actions": [StateFormatter.format_action(action, i) for i, action in enumerate(ctx.deps.playable_actions)], + "strategy_recommendation": ctx.deps.strategy_recommendation if ctx.deps.strategy_recommendation else "No strategy advisor configured", + "strategy_reasoning": ctx.deps.strategy_reasoning if ctx.deps.strategy_reasoning else "No detailed reasoning available", } return full_analysis diff --git a/catanatron/catanatron/players/llm_player.py b/catanatron/catanatron/players/llm_player.py index 4e59e22b..65aeb43a 100644 --- a/catanatron/catanatron/players/llm_player.py +++ b/catanatron/catanatron/players/llm_player.py @@ -78,7 +78,7 @@ def __init__( model: str = "anthropic:claude-sonnet-4-20250514", depth: int = 2, prunning: bool = False, - timeout: Optional[float] = 10.0, + timeout: Optional[float] = 120.0, tool_calls_limit: int = 10, output_mode: Literal["index", "structured"] = "index", is_bot: bool = True, @@ -91,26 +91,22 @@ def __init__( model: LLM model string depth: Search depth for AlphaBeta (higher = slower but better recommendations) prunning: Whether to use alpha-beta pruning - timeout: Timeout in seconds for LLM calls (default: 10.0) + timeout: Timeout in seconds for LLM calls (default: 120.0) tool_calls_limit: Overall tool call limit per decision (default: 10) output_mode: Action output format is_bot: Whether this is a bot player """ # Initialize AlphaBeta first (it will call Player.__init__) - AlphaBetaPlayer.__init__(self, color, depth=depth, prunning=prunning) - - # Then initialize LLM components - self.model = model - self.timeout = timeout - self.tool_calls_limit = tool_calls_limit - self.output_mode = output_mode - self.is_bot = is_bot - - # Import here to avoid circular imports - from catanatron.players.llm.history import ConversationHistoryManager - - self.history_manager = ConversationHistoryManager() - self.agent = self._create_agent() + super().__init__( + color, + model=model, + output_mode=output_mode, + is_bot=is_bot, + timeout=timeout, + tool_calls_limit=tool_calls_limit, + depth=depth, + prunning=prunning, + ) def __repr__(self) -> str: base = AlphaBetaPlayer.__repr__(self) @@ -140,6 +136,8 @@ def __init__( model: str = "anthropic:claude-sonnet-4-20250514", num_simulations: int = 10, prunning: bool = False, + timeout: Optional[float] = 120.0, + tool_calls_limit: int = 10, output_mode: Literal["index", "structured"] = "index", is_bot: bool = True, ): @@ -151,21 +149,22 @@ def __init__( model: LLM model string num_simulations: Number of MCTS simulations (higher = slower but better) prunning: Whether to use action pruning + timeout: Timeout in seconds for LLM calls (default: 120.0) + tool_calls_limit: Overall tool call limit per decision (default: 10) output_mode: Action output format is_bot: Whether this is a bot player """ # Initialize MCTS first - MCTSPlayer.__init__(self, color, num_simulations=num_simulations, prunning=prunning) - - # Then initialize LLM components - self.model = model - self.output_mode = output_mode - self.is_bot = is_bot - - from catanatron.players.llm.history import ConversationHistoryManager - - self.history_manager = ConversationHistoryManager() - self.agent = self._create_agent() + super().__init__( + color, + model=model, + output_mode=output_mode, + is_bot=is_bot, + timeout=timeout, + tool_calls_limit=tool_calls_limit, + num_simulations=num_simulations, + prunning=prunning, + ) def __repr__(self) -> str: base = MCTSPlayer.__repr__(self) @@ -192,6 +191,8 @@ def __init__( model: str = "anthropic:claude-sonnet-4-20250514", value_fn_builder_name: Optional[str] = None, output_mode: Literal["index", "structured"] = "index", + timeout: Optional[float] = 120.0, + tool_calls_limit: int = 10, is_bot: bool = True, ): """ @@ -202,22 +203,21 @@ def __init__( model: LLM model string value_fn_builder_name: Which value function to use ("C" for contender, None for base) output_mode: Action output format + timeout: Timeout in seconds for LLM calls (default: 120.0) + tool_calls_limit: Overall tool call limit per decision (default: 10) is_bot: Whether this is a bot player """ # Initialize ValueFunctionPlayer first - ValueFunctionPlayer.__init__( - self, color, value_fn_builder_name=value_fn_builder_name, is_bot=is_bot + super().__init__( + color, + model=model, + output_mode=output_mode, + is_bot=is_bot, + timeout=timeout, + tool_calls_limit=tool_calls_limit, + value_fn_builder_name=value_fn_builder_name, ) - # Then initialize LLM components - self.model = model - self.output_mode = output_mode - - from catanatron.players.llm.history import ConversationHistoryManager - - self.history_manager = ConversationHistoryManager() - self.agent = self._create_agent() - def __repr__(self) -> str: base = ValueFunctionPlayer.__str__(self) return f"LLM{base}[{self.model}]" From 501dbeecb1ff015e1cad90fad916d2906164003a Mon Sep 17 00:00:00 2001 From: Aditya Chivukula Date: Wed, 14 Jan 2026 19:21:14 +0100 Subject: [PATCH 6/8] update .gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index efaa3648..8a3789c3 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,7 @@ data/ profile.pstats catanatron-venv .DS_Store +catanatron.db # Byte-compiled / optimized / DLL files __pycache__/ From 5621c2eaabaa8f8953af2a0a190bf86941fe4682 Mon Sep 17 00:00:00 2001 From: Aditya Chivukula Date: Thu, 15 Jan 2026 14:04:19 +0100 Subject: [PATCH 7/8] add tenacity --- pyproject.toml | 3 +-- uv.lock | 2 ++ 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index f06033fa..f747d413 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,6 +38,7 @@ llm = [ "openai>=1.50.0", "pydantic-ai>=1.41.0", "logfire>=4.17.0", + "tenacity>=9.1.2", ] dev = [ "black", @@ -57,5 +58,3 @@ catanatron-play = "catanatron.cli.play:simulate" [tool.setuptools.packages.find] where = ["catanatron"] - - diff --git a/uv.lock b/uv.lock index 2a4b5d02..7c40eaa6 100644 --- a/uv.lock +++ b/uv.lock @@ -353,6 +353,7 @@ llm = [ { name = "logfire" }, { name = "openai" }, { name = "pydantic-ai" }, + { name = "tenacity" }, ] web = [ { name = "dill" }, @@ -390,6 +391,7 @@ requires-dist = [ { name = "pytest-watch", marker = "extra == 'dev'" }, { name = "rich" }, { name = "sqlalchemy", marker = "extra == 'web'" }, + { name = "tenacity", marker = "extra == 'llm'", specifier = ">=9.1.2" }, ] provides-extras = ["gym", "web", "llm", "dev"] From 6cca7c063207ee0cfb4316080a8b79f165944f3b Mon Sep 17 00:00:00 2001 From: JanAulichTum Date: Wed, 25 Feb 2026 14:42:25 +0100 Subject: [PATCH 8/8] Elo rating management --- catanatron/catanatron/ELO | 133 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 133 insertions(+) create mode 100644 catanatron/catanatron/ELO diff --git a/catanatron/catanatron/ELO b/catanatron/catanatron/ELO new file mode 100644 index 00000000..f4abf8ac --- /dev/null +++ b/catanatron/catanatron/ELO @@ -0,0 +1,133 @@ +import json +import os + +class CatanArena: + def __init__(self, k_factor=15, save_file="catan_ratings.json"): + self.k_factor = k_factor + self.save_file = save_file + self.ratings = {} + self.h2h = {} + + # Automatically load previous ratings if the file exists + self._load_state() + + def save_state(self): + """Saves the current Elo ratings to a JSON file.""" + with open(self.save_file, 'w') as f: + # We are just saving the ratings here for the leaderboard. + # You could also save self.h2h if you want persistent match histories! + json.dump(self.ratings, f, indent=4) + print(f"šŸ’¾ State successfully saved to {self.save_file}") + + def _load_state(self): + """Loads existing Elo ratings from the JSON file on startup.""" + if os.path.exists(self.save_file): + with open(self.save_file, 'r') as f: + self.ratings = json.load(f) + print(f"šŸ“‚ Loaded existing ratings from {self.save_file}") + + def _ensure_player_exists(self, name): + """Helper to initialize a new player in our trackers.""" + if name not in self.ratings: + self.ratings[name] = 1500.0 + self.h2h[name] = {} + + def record_match(self, match_results): + """ + Processes a single 4-player match, updating Elo and head-to-head stats. + :param match_results: List of dicts, e.g., [{'name': 'GPT-4o', 'vp': 10}, ...] + """ + num_players = len(match_results) + rating_shifts = {p['name']: 0.0 for p in match_results} + + # Ensure all players in this match exist in the system + for p in match_results: + self._ensure_player_exists(p['name']) + + winning_score = max(p['vp'] for p in match_results) + baseline = max(10.0, winning_score) + + # Loop through every 1v1 combination + for i in range(num_players): + for j in range(i + 1, num_players): + p1_name = match_results[i]['name'] + p2_name = match_results[j]['name'] + p1_vp = match_results[i]['vp'] + p2_vp = match_results[j]['vp'] + + # Initialize H2H matchup if it hasn't happened before + if p2_name not in self.h2h[p1_name]: + self.h2h[p1_name][p2_name] = {'wins': 0, 'losses': 0, 'ties': 0} + self.h2h[p2_name][p1_name] = {'wins': 0, 'losses': 0, 'ties': 0} + + # 1. Update Head-to-Head Stats & Determine 1v1 Score + if p1_vp > p2_vp: + s1, s2 = 1.0, 0.0 + self.h2h[p1_name][p2_name]['wins'] += 1 + self.h2h[p2_name][p1_name]['losses'] += 1 + elif p1_vp < p2_vp: + s1, s2 = 0.0, 1.0 + self.h2h[p1_name][p2_name]['losses'] += 1 + self.h2h[p2_name][p1_name]['wins'] += 1 + else: + s1, s2 = 0.5, 0.5 + self.h2h[p1_name][p2_name]['ties'] += 1 + self.h2h[p2_name][p1_name]['ties'] += 1 + + # 2. Calculate Expected Scores + r1 = self.ratings[p1_name] + r2 = self.ratings[p2_name] + exp1 = 1 / (1 + 10 ** ((r2 - r1) / 400)) + exp2 = 1 / (1 + 10 ** ((r1 - r2) / 400)) + + # 3. Calculate MOV Multiplier + vp_diff = abs(p1_vp - p2_vp) + mov_multiplier = (vp_diff / baseline) + 1.0 + + # 4. Calculate Rating Deltas + delta1 = self.k_factor * mov_multiplier * (s1 - exp1) + delta2 = self.k_factor * mov_multiplier * (s2 - exp2) + + rating_shifts[p1_name] += delta1 + rating_shifts[p2_name] += delta2 + + # Apply the averaged rating shifts to the master tracker + for name in rating_shifts: + self.ratings[name] += (rating_shifts[name] / (num_players - 1)) + + def print_h2h_report(self, p1_name, p2_name): + """Prints a clean summary of the matchup between two models.""" + try: + stats = self.h2h[p1_name][p2_name] + total = stats['wins'] + stats['losses'] + stats['ties'] + winrate = (stats['wins'] / total) * 100 if total > 0 else 0 + + print(f"--- {p1_name} vs {p2_name} ---") + print(f"Games played simultaneously: {total}") + print(f"{p1_name} Record: {stats['wins']}W - {stats['losses']}L - {stats['ties']}T") + print(f"{p1_name} Winrate vs {p2_name}: {winrate:.1f}%\n") + except KeyError: + print(f"No match data found between {p1_name} and {p2_name}.\n") + + def generate_leaderboard_from_json(filepath="catan_ratings.json"): + """Opens the saved JSON ratings and prints a formatted leaderboard.""" + try: + with open(filepath, 'r') as f: + ratings = json.load(f) + except FileNotFoundError: + print(f"āŒ Error: Could not find '{filepath}'. Have any games been saved yet?") + return + + # Sort the dictionary by Elo rating, highest to lowest + sorted_ratings = sorted(ratings.items(), key=lambda item: item[1], reverse=True) + + # Print a clean, formatted table + print("\nšŸ† CATAN LLM LEADERBOARD šŸ†") + print("-" * 40) + print(f"{'Rank':<5} | {'Model Name':<15} | {'Elo Rating':<10}") + print("-" * 40) + + for rank, (bot_name, rating) in enumerate(sorted_ratings, start=1): + print(f"{rank:<5} | {bot_name:<15} | {rating:.1f}") + + print("-" * 40 + "\n")