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from typing import Any, Union |
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import gym |
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import numpy as np |
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from ding.envs.env import BaseEnv, BaseEnvTimestep |
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class DemoEnv(BaseEnv): |
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def __init__(self, cfg: dict) -> None: |
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self._closed = True |
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self._observation_space = gym.spaces.Dict( |
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{ |
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"demo_dict": gym.spaces.Tuple( |
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[ |
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gym.spaces.Box(low=-10., high=10., shape=(4, ), dtype=np.float32), |
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gym.spaces.Box(low=-100., high=100., shape=(1, ), dtype=np.float32) |
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] |
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) |
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} |
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) |
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self._action_space = gym.spaces.Discrete(5) |
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self._reward_space = gym.spaces.Box(low=0.0, high=1.0, shape=(1, ), dtype=np.float32) |
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@property |
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def observation_space(self) -> gym.spaces.Space: |
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return self._observation_space |
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@property |
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def action_space(self) -> gym.spaces.Space: |
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return self._action_space |
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@property |
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def reward_space(self) -> gym.spaces.Space: |
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return self._reward_space |
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def reset(self) -> Any: |
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""" |
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Overview: |
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Resets the env to an initial state and returns an initial observation. Abstract Method from ``gym.Env``. |
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""" |
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self._step_count = 0 |
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self._env = "A real environment" |
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self._closed = False |
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return self.observation_space.sample() |
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def close(self) -> None: |
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self._closed = True |
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def step(self, action: Any) -> 'BaseEnv.timestep': |
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self._step_count += 1 |
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obs = self.observation_space.sample() |
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rew = self.reward_space.sample() |
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if self._step_count == 30: |
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self._step_count = 0 |
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done = True |
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else: |
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done = False |
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info = {} |
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if done: |
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info['eval_episode_return'] = self.reward_space.sample() * 30 |
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return BaseEnvTimestep(obs, rew, done, info) |
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def seed(self, seed: int) -> None: |
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self._seed = seed |
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def random_action(self) -> Union[np.ndarray, int]: |
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return self.action_space.sample() |
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def __repr__(self) -> str: |
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return "Demo Env for env_implementation_test.py" |
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