examples/atari/tianshou/env/pettingzoo_env.py

import warnings
from abc import ABC
from typing import Any

import pettingzoo
from gymnasium import spaces
from packaging import version
from pettingzoo.utils.env import AECEnv
from pettingzoo.utils.wrappers import BaseWrapper

if version.parse(pettingzoo.__version__) < version.parse("1.21.0"):
    warnings.warn(
        f"You are using PettingZoo {pettingzoo.__version__}. "
        f"Future tianshou versions may not support PettingZoo<1.21.0. "
        f"Consider upgrading your PettingZoo version.",
        DeprecationWarning,
    )


class PettingZooEnv(AECEnv, 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, trunc, term, 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

        self.rewards = [0] * len(self.agents)

        # Get first observation space, assuming all agents have equal space
        self.observation_space: Any = self.env.observation_space(self.agents[0])

        # Get first action space, assuming all agents have equal space
        self.action_space: Any = self.env.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, *args: Any, **kwargs: Any) -> tuple[dict, dict]:
        self.env.reset(*args, **kwargs)

        observation, reward, terminated, truncated, info = self.env.last(self)

        if isinstance(observation, dict) and "action_mask" in observation:
            observation_dict = {
                "agent_id": self.env.agent_selection,
                "obs": observation["observation"],
                "mask": [obm == 1 for obm in observation["action_mask"]],
            }
        else:
            if isinstance(self.action_space, spaces.Discrete):
                observation_dict = {
                    "agent_id": self.env.agent_selection,
                    "obs": observation,
                    "mask": [True] * self.env.action_space(self.env.agent_selection).n,
                }
            else:
                observation_dict = {
                    "agent_id": self.env.agent_selection,
                    "obs": observation,
                }

        return observation_dict, info

    def step(self, action: Any) -> tuple[dict, list[int], bool, bool, dict]:
        self.env.step(action)

        observation, rew, term, trunc, info = self.env.last()

        if isinstance(observation, dict) and "action_mask" in observation:
            obs = {
                "agent_id": self.env.agent_selection,
                "obs": observation["observation"],
                "mask": [obm == 1 for obm in observation["action_mask"]],
            }
        else:
            if isinstance(self.action_space, 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, term, trunc, info

    def close(self) -> None:
        self.env.close()

    def seed(self, seed: Any = None) -> None:
        try:
            self.env.seed(seed)
        except (NotImplementedError, AttributeError):
            self.env.reset(seed=seed)

    def render(self) -> Any:
        return self.env.render()