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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
@ENV_REGISTRY.register('mujoco_multi')
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
@property
def num_agents(self) -> Any:
return self._num_agents
@property
def observation_space(self) -> gym.spaces.Space:
return self._observation_space
@property
def action_space(self) -> gym.spaces.Space:
return self._action_space
@property
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)
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