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| from typing import Optional, Dict, Any, Tuple | |
| from gym import error | |
| from mlagents_envs.base_env import BaseEnv | |
| from pettingzoo import ParallelEnv | |
| from mlagents_envs.envs.unity_pettingzoo_base_env import UnityPettingzooBaseEnv | |
| class UnityParallelEnv(UnityPettingzooBaseEnv, ParallelEnv): | |
| """ | |
| Unity Parallel (PettingZoo) environment wrapper. | |
| """ | |
| def __init__(self, env: BaseEnv, seed: Optional[int] = None): | |
| """ | |
| Initializes a Unity Parallel environment wrapper. | |
| :param env: The UnityEnvironment that is being wrapped. | |
| :param seed: The seed for the action spaces of the agents. | |
| """ | |
| super().__init__(env, seed) | |
| def reset(self) -> Dict[str, Any]: | |
| """ | |
| Resets the environment. | |
| """ | |
| super().reset() | |
| return self._observations | |
| def step(self, actions: Dict[str, Any]) -> Tuple: | |
| self._assert_loaded() | |
| if len(self._live_agents) <= 0 and actions: | |
| raise error.Error( | |
| "You must reset the environment before you can perform a step." | |
| ) | |
| # Process actions | |
| for current_agent, action in actions.items(): | |
| self._process_action(current_agent, action) | |
| # Reset reward | |
| for k in self._rewards.keys(): | |
| self._rewards[k] = 0 | |
| # Step environment | |
| self._step() | |
| # Agent cleanup and sorting | |
| self._cleanup_agents() | |
| self._live_agents.sort() # unnecessary, only for passing API test | |
| return self._observations, self._rewards, self._dones, self._infos | |