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| import sys | |
| from typing import Any, List, Optional, Union | |
| import gym | |
| import gym_soccer | |
| import numpy as np | |
| from ding.envs import BaseEnv, BaseEnvInfo, BaseEnvTimestep | |
| from ding.envs.common.common_function import affine_transform | |
| from ding.envs.common.env_element import EnvElementInfo | |
| from ding.torch_utils import to_list, to_ndarray, to_tensor | |
| from ding.utils import ENV_REGISTRY | |
| from gym.utils import seeding | |
| import copy | |
| class GymSoccerEnv(BaseEnv): | |
| default_env_id = ['Soccer-v0', 'SoccerEmptyGoal-v0', 'SoccerAgainstKeeper-v0'] | |
| def __init__(self, cfg: dict = {}) -> None: | |
| self._cfg = cfg | |
| self._act_scale = cfg.act_scale | |
| self._env_id = cfg.env_id | |
| assert self._env_id in self.default_env_id | |
| self._init_flag = False | |
| self._replay_path = './game_log' | |
| def reset(self) -> np.array: | |
| if not self._init_flag: | |
| self._env = gym.make(self._env_id, replay_path=self._replay_path, port=self._cfg.port) # TODO | |
| self._init_flag = True | |
| self._eval_episode_return = 0 | |
| obs = self._env.reset() | |
| obs = to_ndarray(obs).astype(np.float32) | |
| return obs | |
| def step(self, action: List) -> BaseEnvTimestep: | |
| if self._act_scale: | |
| # The continuous action is a Tensor of size = (1,) | |
| # We indexed at [0] to fetch it as a scalar value | |
| action[1][0] = affine_transform(action[1][0], min_val=0, max_val=100) | |
| action[2][0] = affine_transform(action[2][0], min_val=-180, max_val=180) | |
| action[3][0] = affine_transform(action[3][0], min_val=-180, max_val=180) | |
| action[4][0] = affine_transform(action[4][0], min_val=0, max_val=100) | |
| action[5][0] = affine_transform(action[5][0], min_val=-180, max_val=180) | |
| obs, rew, done, info = self._env.step(action) | |
| self._eval_episode_return += rew | |
| if done: | |
| info['eval_episode_return'] = self._eval_episode_return | |
| obs = to_ndarray(obs).astype(np.float32) | |
| # reward wrapped to be transfered to a numpy array with shape (1,) | |
| rew = to_ndarray([rew]) | |
| # '1' indicates the discrete action is associated with the continuous parameters | |
| info['action_args_mask'] = np.array([[1, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 1]]) | |
| return BaseEnvTimestep(obs, rew, done, info) | |
| def seed(self, seed: int, dynamic_seed: bool = True) -> None: | |
| self._seed = seed | |
| self._dynamic_seed = dynamic_seed | |
| np.random.seed(self._seed) | |
| def close(self) -> None: | |
| self._init_flag = False | |
| def get_random_action(self): | |
| # discrete action type: 0, 1, 2 | |
| # continuous action_args: | |
| # - power: [0, 100] | |
| # - direction: [-180, 180] | |
| # the action space is (6,), the first indicates discrete action and the remaining indicates continuous action | |
| # discrete action 0 assotiated with the first and second continuous parameters | |
| # discrete action 1 assotiated with the third continuous parameter | |
| # discrete action 2 assotiated with the forth and fifth continuous parameters | |
| return self._env.action_space.sample() | |
| def info(self) -> BaseEnvInfo: | |
| T = EnvElementInfo | |
| return BaseEnvInfo( | |
| agent_num=1, | |
| obs_space=T( | |
| (59, ), | |
| { | |
| # [min, max] | |
| 'min': -1, | |
| 'max': 1, | |
| 'dtype': np.float32, | |
| }, | |
| ), | |
| act_space=T( | |
| # the discrete action shape is (3,) | |
| # however, the continuous action shape is (5,), which is not revealed in the info | |
| ( | |
| 3, | |
| ), | |
| { | |
| # [min, max) | |
| 'min': 0, | |
| 'max': 3, | |
| 'dtype': int, | |
| }, | |
| ), | |
| rew_space=T( | |
| (1, ), | |
| { | |
| # [min, max) | |
| 'min': 0, | |
| 'max': 2.0, | |
| 'dtype': int, | |
| }, | |
| ), | |
| use_wrappers=None, | |
| ) | |
| def render(self, close=False): | |
| self._env.render(close) | |
| def __repr__(self) -> str: | |
| return "DI-engine gym soccer Env" | |
| def replay_log(self, log_path): | |
| self._env.replay_log(log_path) | |
| def enable_save_replay(self, replay_path: Optional[str] = None) -> None: | |
| if replay_path is None: | |
| replay_path = './game_log' | |
| self._replay_path = replay_path | |
| def create_collector_env_cfg(cfg: dict) -> List[dict]: | |
| """ | |
| Overview: | |
| Return a list of all of the environment from input config. | |
| Arguments: | |
| - cfg (:obj:`Dict`) Env config, same config where ``self.__init__()`` takes arguments from | |
| Returns: | |
| - List of ``cfg`` including all of the collector env's config | |
| """ | |
| cfg_list = [] | |
| collector_env_num = cfg.pop('collector_env_num') | |
| port_pool = list(range(6000, 9999)) | |
| port_candidates = np.random.choice(port_pool, size=collector_env_num, replace=False) | |
| for i in range(collector_env_num): | |
| cfg_copy = copy.deepcopy(cfg) | |
| cfg_copy.port = port_candidates[i] | |
| cfg_list.append(cfg_copy) | |
| return cfg_list | |
| def create_evaluator_env_cfg(cfg: dict) -> List[dict]: | |
| """ | |
| Overview: | |
| Return a list of all of the environment from input config. | |
| Arguments: | |
| - cfg (:obj:`Dict`) Env config, same config where ``self.__init__()`` takes arguments from | |
| Returns: | |
| - List of ``cfg`` including all of the evaluator env's config | |
| """ | |
| cfg_list = [] | |
| evaluator_env_num = cfg.pop('evaluator_env_num') | |
| port_pool = list(range(6000, 9999)) | |
| port_candidates = np.random.choice(port_pool, size=evaluator_env_num, replace=False) | |
| for i in range(evaluator_env_num): | |
| cfg_copy = copy.deepcopy(cfg) | |
| cfg_copy.port = port_candidates[i] | |
| cfg_list.append(cfg_copy) | |
| return cfg_list | |