from abc import ABC from typing import Any, Dict, List, Tuple import gym.spaces from pettingzoo.utils.env import AECEnv from pettingzoo.utils.wrappers import BaseWrapper class PettingZooEnv(AECEnv, gym.Env, ABC): """The interface for petting zoo environments. Multi-agent environments must be wrapped as :class:`~tianshou.env.PettingZooEnv`. Here is the usage: :: env = PettingZooEnv(...) # obs is a dict containing obs, agent_id, and mask obs = env.reset() action = policy(obs) obs, rew, done, info = env.step(action) env.close() The available action's mask is set to True, otherwise it is set to False. Further usage can be found at :ref:`marl_example`. """ def __init__(self, env: BaseWrapper): super().__init__() self.env = env # agent idx list self.agents = self.env.possible_agents self.agent_idx = {} for i, agent_id in enumerate(self.agents): self.agent_idx[agent_id] = i # Get dictionaries of obs_spaces and act_spaces self.observation_spaces = self.env.observation_spaces self.action_spaces = self.env.action_spaces self.rewards = [0] * len(self.agents) # Get first observation space, assuming all agents have equal space self.observation_space: Any = self.observation_space(self.agents[0]) # Get first action space, assuming all agents have equal space self.action_space: Any = self.action_space(self.agents[0]) assert all(self.env.observation_space(agent) == self.observation_space for agent in self.agents), \ "Observation spaces for all agents must be identical. Perhaps " \ "SuperSuit's pad_observations wrapper can help (useage: " \ "`supersuit.aec_wrappers.pad_observations(env)`" assert all(self.env.action_space(agent) == self.action_space for agent in self.agents), \ "Action spaces for all agents must be identical. Perhaps " \ "SuperSuit's pad_action_space wrapper can help (useage: " \ "`supersuit.aec_wrappers.pad_action_space(env)`" self.reset() def reset(self) -> dict: self.env.reset() observation = self.env.observe(self.env.agent_selection) if isinstance(observation, dict) and 'action_mask' in observation: return { 'agent_id': self.env.agent_selection, 'obs': observation['observation'], 'mask': [True if obm == 1 else False for obm in observation['action_mask']] } else: if isinstance(self.action_space, gym.spaces.Discrete): return { 'agent_id': self.env.agent_selection, 'obs': observation, 'mask': [True] * self.env.action_space(self.env.agent_selection).n } else: return {'agent_id': self.env.agent_selection, 'obs': observation} def step(self, action: Any) -> Tuple[Dict, List[int], bool, Dict]: self.env.step(action) observation, rew, done, info = self.env.last() if isinstance(observation, dict) and 'action_mask' in observation: obs = { 'agent_id': self.env.agent_selection, 'obs': observation['observation'], 'mask': [True if obm == 1 else False for obm in observation['action_mask']] } else: if isinstance(self.action_space, gym.spaces.Discrete): obs = { 'agent_id': self.env.agent_selection, 'obs': observation, 'mask': [True] * self.env.action_space(self.env.agent_selection).n } else: obs = {'agent_id': self.env.agent_selection, 'obs': observation} for agent_id, reward in self.env.rewards.items(): self.rewards[self.agent_idx[agent_id]] = reward return obs, self.rewards, done, info def close(self) -> None: self.env.close() def seed(self, seed: Any = None) -> None: self.env.seed(seed) def render(self, mode: str = "human") -> Any: return self.env.render(mode)