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from typing import Any, List, Union, Optional
import time
import gym
import numpy as np
from ding.envs import BaseEnv, BaseEnvTimestep, FrameStackWrapper
from ding.torch_utils import to_ndarray, to_list
from ding.envs.common.common_function import affine_transform
from ding.utils import ENV_REGISTRY
@ENV_REGISTRY.register('bipedalwalker')
class BipedalWalkerEnv(BaseEnv):
def __init__(self, cfg: dict) -> None:
self._cfg = cfg
self._init_flag = False
self._act_scale = cfg.act_scale
self._rew_clip = cfg.rew_clip
if "replay_path" in cfg:
self._replay_path = cfg.replay_path
else:
self._replay_path = None
def reset(self) -> np.ndarray:
if not self._init_flag:
self._env = gym.make('BipedalWalker-v3')
self._observation_space = self._env.observation_space
self._action_space = self._env.action_space
self._reward_space = gym.spaces.Box(
low=self._env.reward_range[0], high=self._env.reward_range[1], shape=(1, ), dtype=np.float32
)
self._init_flag = True
if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed:
np_seed = 100 * np.random.randint(1, 1000)
self._env.seed(self._seed + np_seed)
elif hasattr(self, '_seed'):
self._env.seed(self._seed)
if self._replay_path is not None:
self._env = gym.wrappers.RecordVideo(
self._env,
video_folder=self._replay_path,
episode_trigger=lambda episode_id: True,
name_prefix='rl-video-{}'.format(id(self))
)
self._eval_episode_return = 0
obs = self._env.reset()
obs = to_ndarray(obs).astype(np.float32)
return obs
def close(self) -> None:
if self._init_flag:
self._env.close()
self._init_flag = False
def render(self) -> None:
self._env.render()
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: np.ndarray) -> BaseEnvTimestep:
assert isinstance(action, np.ndarray), type(action)
if action.shape == (1, ):
action = action.squeeze() # 0-dim array
if self._act_scale:
action = affine_transform(action, min_val=self.action_space.low, max_val=self.action_space.high)
obs, rew, done, info = self._env.step(action)
self._eval_episode_return += rew
if self._rew_clip:
rew = max(-10, rew)
rew = np.float32(rew)
if done:
info['eval_episode_return'] = self._eval_episode_return
obs = to_ndarray(obs).astype(np.float32)
rew = to_ndarray([rew]) # wrapped to be transfered to a array with shape (1,)
return BaseEnvTimestep(obs, rew, done, info)
def enable_save_replay(self, replay_path: Optional[str] = None) -> None:
if replay_path is None:
replay_path = './video'
self._replay_path = replay_path
def random_action(self) -> np.ndarray:
random_action = self.action_space.sample()
if isinstance(random_action, np.ndarray):
pass
elif isinstance(random_action, int):
random_action = to_ndarray([random_action], dtype=np.int64)
return random_action
@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 BipedalWalker Env"
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