import copy
import os
from datetime import datetime
from typing import List, Optional
import gymnasium as gym
import matplotlib.pyplot as plt
import numpy as np
from ding.envs import BaseEnv, BaseEnvTimestep
from ding.envs import ObsPlusPrevActRewWrapper
from ding.torch_utils import to_ndarray
from ding.utils import ENV_REGISTRY
from dizoo.minigrid.envs.minigrid_wrapper import ViewSizeWrapper
from dizoo.minigrid.envs.minigrid_env import MiniGridEnv
from easydict import EasyDict
from matplotlib import animation
from minigrid.wrappers import FlatObsWrapper
@ENV_REGISTRY.register('minigrid_lightzero')
class MiniGridEnvLightZero(MiniGridEnv):
"""
Overview:
A MiniGrid environment for LightZero, based on OpenAI Gym.
Attributes:
config (dict): Configuration dict. Default configurations can be updated using this.
_cfg (dict): Internal configuration dict that stores runtime configurations.
_init_flag (bool): Flag to check if the environment is initialized.
_env_name (str): The name of the MiniGrid environment.
_flat_obs (bool): Flag to check if flat observations are returned.
_save_replay (bool): Flag to check if replays are saved.
_max_step (int): Maximum number of steps for the environment.
"""
config = dict(
# (str) The gym environment name.
env_name='MiniGrid-Empty-8x8-v0',
# (bool) If True, save the replay as a gif file.
save_replay_gif=False,
# (str or None) The path to save the replay gif. If None, the replay gif will not be saved.
replay_path_gif=None,
flat_obs=True,
# (int) The maximum number of steps for each episode.
max_step=300,
)
@classmethod
def default_config(cls: type) -> EasyDict:
"""
Overview:
Returns the default configuration with the current environment class name.
Returns:
- cfg (:obj:`dict`): Configuration dict.
"""
cfg = EasyDict(copy.deepcopy(cls.config))
cfg.cfg_type = cls.__name__ + 'Dict'
return cfg
def __init__(self, cfg: dict) -> None:
"""
Overview:
Initialize the environment.
Arguments:
- cfg (:obj:`dict`): Configuration dict. The configuration should include the environment name,
whether to use flat observations, and the maximum number of steps.
"""
self._cfg = cfg
self._init_flag = False
self._env_name = cfg.env_name
self._flat_obs = cfg.flat_obs
self._save_replay_gif = cfg.save_replay_gif
self._replay_path_gif = cfg.replay_path_gif
self._max_step = cfg.max_step
self._save_replay_count = 0
def reset(self) -> np.ndarray:
"""
Overview:
Reset the environment and return the initial observation.
Returns:
- obs (:obj:`np.ndarray`): Initial observation from the environment.
"""
if not self._init_flag:
if self._save_replay_gif:
self._env = gym.make(self._env_name, render_mode="rgb_array")
else:
self._env = gym.make(self._env_name)
# NOTE: customize the max step of the env
self._env.max_steps = self._max_step
if self._env_name 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_name == '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 = ImgObsWrapper(self._env)
# self._env = RGBImgPartialObsWrapper(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, ))
else:
self._observation_space = self._env.observation_space
# to be compatible 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
)
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._eval_episode_return = 0
self._current_step = 0
if self._save_replay_gif:
self._frames = []
action_mask = np.ones(self.action_space.n, 'int8')
obs = {'observation': obs, 'action_mask': action_mask, 'to_play': -1}
return obs
def close(self) -> None:
"""
Close the environment, and set the initialization flag to False.
"""
if self._init_flag:
self._env.close()
self._init_flag = False
def seed(self, seed: int, dynamic_seed: bool = True) -> None:
"""
Set the seed for the environment's random number generator. Can handle both static and dynamic seeding.
"""
self._seed = seed
self._dynamic_seed = dynamic_seed
np.random.seed(self._seed)
def step(self, action: np.ndarray) -> BaseEnvTimestep:
"""
Overview:
Perform a step in the environment using the provided action, and return the next state of the environment.
The next state is encapsulated in a BaseEnvTimestep object, which includes the new observation, reward,
done flag, and info dictionary.
Arguments:
- action (:obj:`np.ndarray`): The action to be performed in the environment.
Returns:
- timestep (:obj:`BaseEnvTimestep`): An object containing the new observation, reward, done flag,
and info dictionary.
.. note::
- The cumulative reward (`_eval_episode_return`) is updated with the reward obtained in this step.
- If the episode ends (done is True), the total reward for the episode is stored in the info dictionary
under the key 'eval_episode_return'.
- An action mask is created with ones, which represents the availability of each action in the action space.
- Observations are returned in a dictionary format containing 'observation', 'action_mask', and 'to_play'.
"""
if isinstance(action, np.ndarray) and action.shape == (1, ):
action = action.squeeze() # 0-dim array
if self._save_replay_gif:
self._frames.append(self._env.render())
# using the step method of Gymnasium env, return is (observation, reward, terminated, truncated, info)
obs, rew, terminated, truncated, info = self._env.step(action)
done = terminated or truncated
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_gif:
if not os.path.exists(self._replay_path_gif):
os.makedirs(self._replay_path_gif)
timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
path = os.path.join(
self._replay_path_gif,
'{}_episode_{}_seed{}_{}.gif'.format(self._env_name, self._save_replay_count, self._seed, timestamp)
)
self.display_frames_as_gif(self._frames, path)
print(f'save episode {self._save_replay_count} in {self._replay_path_gif}!')
self._save_replay_count += 1
obs = to_ndarray(obs)
rew = to_ndarray([rew]) # wrapped to be transferred to an array with shape (1,)
action_mask = np.ones(self.action_space.n, 'int8')
obs = {'observation': obs, 'action_mask': action_mask, 'to_play': -1}
return BaseEnvTimestep(obs, rew, done, info)
def random_action(self) -> np.ndarray:
"""
Generate a random action using the action space's sample method. Returns a numpy array containing the action.
"""
random_action = self.action_space.sample()
random_action = to_ndarray([random_action], dtype=np.int64)
return random_action
def enable_save_replay(self, replay_path: Optional[str] = None) -> None:
"""
Enable the saving of replay videos. If no replay path is given, a default is used.
"""
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)
@property
def observation_space(self) -> gym.spaces.Space:
"""
Property to access the observation space of the environment.
"""
return self._observation_space
@property
def action_space(self) -> gym.spaces.Space:
"""
Property to access the action space of the environment.
"""
return self._action_space
@property
def reward_space(self) -> gym.spaces.Space:
"""
Property to access the reward space of the environment.
"""
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:
"""
String representation of the environment.
"""
return "LightZero MiniGrid Env({})".format(self._cfg.env_name)