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| from typing import Any, Union, List | |
| import copy | |
| import numpy as np | |
| from numpy import dtype | |
| import gym | |
| from ding.envs import BaseEnv, BaseEnvTimestep | |
| from ding.envs.common.common_function import affine_transform | |
| from ding.torch_utils import to_ndarray, to_list | |
| from ding.utils import ENV_REGISTRY | |
| from .mujoco_multi import MujocoMulti | |
| class MujocoEnv(BaseEnv): | |
| def __init__(self, cfg: dict) -> None: | |
| self._cfg = cfg | |
| self._init_flag = False | |
| def reset(self) -> np.ndarray: | |
| if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed: | |
| np_seed = 100 * np.random.randint(1, 1000) | |
| self._cfg.seed = self._seed + np_seed | |
| elif hasattr(self, '_seed'): | |
| self._cfg.seed = self._seed | |
| if not self._init_flag: | |
| self._env = MujocoMulti(env_args=self._cfg) | |
| self._init_flag = True | |
| obs = self._env.reset() | |
| self._eval_episode_return = 0. | |
| # TODO: | |
| # self.env_info for scenario='Ant-v2', agent_conf="2x4d", | |
| # {'state_shape': 2, 'obs_shape': 54,...} | |
| # 'state_shape' is wrong, it should be 111 | |
| self.env_info = self._env.get_env_info() | |
| # self._env.observation_space[agent].shape equals above 'state_shape' | |
| self._num_agents = self.env_info['n_agents'] | |
| self._agents = [i for i in range(self._num_agents)] | |
| self._observation_space = gym.spaces.Dict( | |
| { | |
| 'agent_state': gym.spaces.Box( | |
| low=float("-inf"), high=float("inf"), shape=obs['agent_state'].shape, dtype=np.float32 | |
| ), | |
| 'global_state': gym.spaces.Box( | |
| low=float("-inf"), high=float("inf"), shape=obs['global_state'].shape, dtype=np.float32 | |
| ), | |
| } | |
| ) | |
| self._action_space = gym.spaces.Dict({agent: self._env.action_space[agent] for agent in self._agents}) | |
| single_agent_obs_space = self._env.action_space[self._agents[0]] | |
| if isinstance(single_agent_obs_space, gym.spaces.Box): | |
| self._action_dim = single_agent_obs_space.shape | |
| elif isinstance(single_agent_obs_space, gym.spaces.Discrete): | |
| self._action_dim = (single_agent_obs_space.n, ) | |
| else: | |
| raise Exception('Only support `Box` or `Discrte` obs space for single agent.') | |
| self._reward_space = gym.spaces.Dict( | |
| { | |
| agent: gym.spaces.Box(low=float("-inf"), high=float("inf"), shape=(1, ), dtype=np.float32) | |
| for agent in self._agents | |
| } | |
| ) | |
| return obs | |
| def close(self) -> None: | |
| if self._init_flag: | |
| self._env.close() | |
| self._init_flag = False | |
| def seed(self, seed: int, dynamic_seed: bool = True) -> None: | |
| self._seed = seed | |
| self._dynamic_seed = dynamic_seed | |
| np.random.seed(self._seed) | |
| def step(self, action: Union[np.ndarray, list]) -> BaseEnvTimestep: | |
| action = to_ndarray(action) | |
| obs, rew, done, info = self._env.step(action) | |
| self._eval_episode_return += rew | |
| rew = to_ndarray([rew]) # wrapped to be transfered to a array with shape (1,) | |
| if done: | |
| info['eval_episode_return'] = self._eval_episode_return | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| def random_action(self) -> np.ndarray: | |
| random_action = self.action_space.sample() | |
| random_action = to_ndarray([random_action], dtype=np.int64) | |
| return random_action | |
| def num_agents(self) -> Any: | |
| return self._num_agents | |
| def observation_space(self) -> gym.spaces.Space: | |
| return self._observation_space | |
| def action_space(self) -> gym.spaces.Space: | |
| return self._action_space | |
| def reward_space(self) -> gym.spaces.Space: | |
| return self._reward_space | |
| def __repr__(self) -> str: | |
| return "DI-engine Multi-agent Mujoco Env({})".format(self._cfg.env_id) | |