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from typing import Any, List, Union, Optional
from collections import namedtuple
from easydict import EasyDict
import copy
import os
import time
import gymnasium as gym
import numpy as np
from matplotlib import animation
import matplotlib.pyplot as plt
from minigrid.wrappers import FlatObsWrapper, RGBImgPartialObsWrapper, ImgObsWrapper
from .minigrid_wrapper import ViewSizeWrapper
from ding.envs import ObsPlusPrevActRewWrapper
from ding.envs import BaseEnv, BaseEnvTimestep
from ding.torch_utils import to_ndarray, to_list
from ding.utils import ENV_REGISTRY
@ENV_REGISTRY.register('minigrid')
class MiniGridEnv(BaseEnv):
config = dict(
env_id='MiniGrid-KeyCorridorS3R3-v0',
flat_obs=True,
)
@classmethod
def default_config(cls: type) -> EasyDict:
cfg = EasyDict(copy.deepcopy(cls.config))
cfg.cfg_type = cls.__name__ + 'Dict'
return cfg
def __init__(self, cfg: dict) -> None:
self._cfg = cfg
self._init_flag = False
self._env_id = cfg.env_id
self._flat_obs = cfg.flat_obs
self._save_replay = False
self._max_step = cfg.max_step
def reset(self) -> np.ndarray:
if not self._init_flag:
if self._save_replay:
self._env = gym.make(self._env_id, render_mode="rgb_array") # using the Gymnasium make method
else:
self._env = gym.make(self._env_id)
if self._env_id in ['MiniGrid-AKTDT-13x13-v0' or 'MiniGrid-AKTDT-13x13-1-v0']:
# customize the agent field of view size, note this must be an odd number
# This also related to the observation space, see gym_minigrid.wrappers for more details
self._env = ViewSizeWrapper(self._env, agent_view_size=5)
if self._env_id == 'MiniGrid-AKTDT-7x7-1-v0':
self._env = ViewSizeWrapper(self._env, agent_view_size=3)
if self._flat_obs:
self._env = FlatObsWrapper(self._env)
# self._env = RGBImgPartialObsWrapper(self._env)
# self._env = ImgObsWrapper(self._env)
if hasattr(self._cfg, 'obs_plus_prev_action_reward') and self._cfg.obs_plus_prev_action_reward:
self._env = ObsPlusPrevActRewWrapper(self._env)
self._init_flag = True
if self._flat_obs:
self._observation_space = gym.spaces.Box(0, 1, shape=(2835, ), dtype=np.float32)
else:
self._observation_space = self._env.observation_space
# to be compatiable with subprocess env manager
if isinstance(self._observation_space, gym.spaces.Dict):
self._observation_space['obs'].dtype = np.dtype('float32')
else:
self._observation_space.dtype = np.dtype('float32')
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._eval_episode_return = 0
if hasattr(self, '_seed') and hasattr(self, '_dynamic_seed') and self._dynamic_seed:
np_seed = 100 * np.random.randint(1, 1000)
self._seed = self._seed + np_seed
obs, _ = self._env.reset(seed=self._seed) # using the reset method of Gymnasium env
elif hasattr(self, '_seed'):
obs, _ = self._env.reset(seed=self._seed)
else:
obs, _ = self._env.reset()
obs = to_ndarray(obs)
self._current_step = 0
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: np.ndarray) -> BaseEnvTimestep:
assert isinstance(action, np.ndarray), type(action)
if action.shape == (1, ):
action = action.squeeze() # 0-dim array
if self._save_replay:
self._frames.append(self._env.render())
# using the step method of Gymnasium env, return is (observation, reward, terminated, truncated, info)
obs, rew, done, _, info = self._env.step(action)
rew = float(rew)
self._eval_episode_return += rew
self._current_step += 1
if self._current_step >= self._max_step:
done = True
if done:
info['eval_episode_return'] = self._eval_episode_return
info['current_step'] = self._current_step
info['max_step'] = self._max_step
if self._save_replay:
path = os.path.join(
self._replay_path, '{}_episode_{}.gif'.format(self._env_id, self._save_replay_count)
)
self.display_frames_as_gif(self._frames, path)
self._save_replay_count += 1
obs = to_ndarray(obs)
rew = to_ndarray([rew]) # wrapped to be transferred to a array with shape (1,)
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 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
@staticmethod
def create_collector_env_cfg(cfg: dict) -> List[dict]:
collector_env_num = cfg.pop('collector_env_num')
cfg = copy.deepcopy(cfg)
cfg.is_train = True
return [cfg for _ in range(collector_env_num)]
@staticmethod
def create_evaluator_env_cfg(cfg: dict) -> List[dict]:
evaluator_env_num = cfg.pop('evaluator_env_num')
cfg = copy.deepcopy(cfg)
cfg.is_train = False
return [cfg for _ in range(evaluator_env_num)]
def __repr__(self) -> str:
return "DI-engine MiniGrid Env({})".format(self._cfg.env_id)
def enable_save_replay(self, replay_path: Optional[str] = None) -> None:
if replay_path is None:
replay_path = './video'
self._save_replay = True
self._replay_path = replay_path
self._save_replay_count = 0
@staticmethod
def display_frames_as_gif(frames: list, path: str) -> None:
patch = plt.imshow(frames[0])
plt.axis('off')
def animate(i):
patch.set_data(frames[i])
anim = animation.FuncAnimation(plt.gcf(), animate, frames=len(frames), interval=5)
anim.save(path, writer='imagemagick', fps=20)
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