reward_function.py

"""
This function is intended to wrap the rewards returned by the CityLearn RL environment, and is meant to 
be modified at will. This reward_function takes all the electrical demands and carbon intensity of all the buildings and turns them into one or multiple rewards for the agent(s)
"""
import numpy as np

# Reward used in the CityLearn Challenge. Reward function for the multi-agent (decentralized) agents.
class reward_function_ma:
    def __init__(self, n_agents, building_info):
        self.n_agents = n_agents
        self.building_info = building_info

    # electricity_demand contains negative values when the building consumes more electricity than it generates
    def get_rewards(self, electricity_demand, carbon_intensity):
        
        # You can edit what comes next and customize it for The CityLearn Challenge
        electricity_demand = np.float32(electricity_demand)
        total_electricity_demand = 0
        for e in electricity_demand:
            total_electricity_demand += -e
            
        electricity_demand = np.array(electricity_demand)
        
        using_marlisa = False
        # Use this reward function when running the MARLISA example with information_sharing = True. The reward sent to each agent will have an individual and a collective component.
        if using_marlisa:
            return list(np.sign(electricity_demand)*0.01*(np.array(np.abs(electricity_demand))**2 * max(0, total_electricity_demand)))
        
        else:
            
            # Use this reward when running the SAC example. It assumes that the building-agents act independently of each other, without sharing information through the reward.
            reward_ = np.array(electricity_demand)**3.0
            reward_[reward_>0] = 0
            return list(reward_)
    
      
        
        
        
        
        
        
        
        
# Do not use or delete
# Reward function for the centralized agent. To be used only if all the buildings receive the same reward.
def reward_function_sa(electricity_demand):

    reward_ = -np.array(electricity_demand).sum()
    reward_ = max(0, reward_)
    reward_ = reward_**3.0
    
    return reward_