Source code for pettingzoo.utils.average_total_reward

from __future__ import annotations

import random

import numpy as np

from pettingzoo.utils.env import AECEnv

[docs] def average_total_reward( env: AECEnv, max_episodes: int = 100, max_steps: int = 10000000000 ) -> float: """Calculates the average total reward over the episodes for AEC environments. Runs an env object with random actions until either max_episodes or max_steps is reached. Reward is summed across all agents, making it unsuited for use in zero-sum games. """ total_reward = 0 total_steps = 0 num_episodes = 0 for episode in range(max_episodes): if total_steps >= max_steps: break env.reset() for agent in env.agent_iter(): # Because we call env.last() this function only works with AEC envs obs, reward, termination, truncation, _ = env.last(observe=False) total_reward += reward total_steps += 1 if termination or truncation: action = None elif isinstance(obs, dict) and "action_mask" in obs: action = random.choice(np.flatnonzero(obs["action_mask"]).tolist()) else: action = env.action_space(agent).sample() env.step(action) num_episodes = episode + 1 print("Average total reward", total_reward / num_episodes) return total_reward / num_episodes