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from typing import Any, List, Union, Optional
import time
import os
import imageio
import gym
import copy
import numpy as np
from easydict import EasyDict
from rocket_recycling.rocket import Rocket
from ding.envs import BaseEnv, BaseEnvTimestep
from ding.torch_utils import to_ndarray, to_list
from ding.utils import ENV_REGISTRY
from ding.envs import ObsPlusPrevActRewWrapper
@ENV_REGISTRY.register('rocket', force_overwrite=True)
class RocketEnv(BaseEnv):
def __init__(self, cfg: dict = {}) -> None:
self._cfg = cfg
self._init_flag = False
self._save_replay = False
self._observation_space = gym.spaces.Box(low=float("-inf"), high=float("inf"), shape=(8, ), dtype=np.float32)
self._action_space = gym.spaces.Discrete(9)
self._action_space.seed(0) # default seed
self._reward_space = gym.spaces.Box(low=float("-inf"), high=float("inf"), shape=(1, ), dtype=np.float32)
def reset(self) -> np.ndarray:
if not self._init_flag:
self._env = Rocket(task=self._cfg.task, max_steps=self._cfg.max_steps)
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)
self._action_space.seed(self._seed + np_seed)
elif hasattr(self, '_seed'):
self._env.seed(self._seed)
self._action_space.seed(self._seed)
self._eval_episode_return = 0
obs = self._env.reset()
obs = to_ndarray(obs)
if self._save_replay:
self._frames = []
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[int, np.ndarray]) -> BaseEnvTimestep:
if isinstance(action, np.ndarray) and action.shape == (1, ):
action = action.squeeze() # 0-dim array
obs, rew, done, info = self._env.step(action)
self._env.render()
self._eval_episode_return += rew
if self._save_replay:
self._frames.extend(self._env.render())
if done:
info['eval_episode_return'] = self._eval_episode_return
if self._save_replay:
path = os.path.join(self._replay_path, '{}_episode.gif'.format(self._save_replay_count))
self.display_frames_as_gif(self._frames, path)
self._save_replay_count += 1
obs = to_ndarray(obs)
# wrapped to be transfered to a array with shape (1,)
rew = to_ndarray([rew]).astype(np.float32)
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._save_replay = True
if not os.path.exists(replay_path):
os.makedirs(replay_path)
self._replay_path = replay_path
self._save_replay_count = 0
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 clone(self, caller: str) -> 'RocketEnv':
return RocketEnv(copy.deepcopy(self._cfg))
@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 Rocket Env"
@staticmethod
def display_frames_as_gif(frames: list, path: str) -> None:
imageio.mimsave(path, frames, fps=20)
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