fix(pu): fix render path bug in tictactoe and cartpole

zoo/atari/envs/atari_lightzero_env.py

@@ -44,8 +44,9 @@ class AtariLightZeroEnv(BaseEnv):
         render_mode_human=False,
         # (bool) If True, a video of the game play is saved.
         save_replay=False,
-        # (str) The path to save the video.
-        replay_path='./video',
+        # replay_path (str or None): The path to save the replay video. If None, the replay will not be saved.
+        # Only effective when env_manager.type is 'base'.
+        replay_path=None,
         # (bool) If set to True, the game screen is converted to grayscale, reducing the complexity of the observation space.
         gray_scale=True,
         # (int) The number of frames to skip between each action. Higher values result in faster simulation.

zoo/board_games/connect4/envs/connect4_env.py

@@ -47,9 +47,6 @@
 from zoo.board_games.mcts_bot import MCTSBot
 
 
-
-
-
 @ENV_REGISTRY.register('connect4')
 class Connect4Env(BaseEnv):
     config = dict(
@@ -62,8 +59,8 @@ class Connect4Env(BaseEnv):
         # (str) The render mode. Options are 'None', 'state_realtime_mode', 'image_realtime_mode' or 'image_savefile_mode'.
         # If None, then the game will not be rendered.
         render_mode=None,
-        # (str) The suffix of the replay name.
-        replay_path='./video',
+        # (str or None) The directory in which to save the replay file. If None, the file is saved in the current directory.
+        replay_path=None,
         # (str) The type of the bot of the environment.
         bot_action_type='rule',
         # (bool) Whether to let human to play with the agent when evaluating. If False, then use the bot to evaluate the agent.
@@ -479,7 +476,7 @@ def save_render_output(self, replay_name_suffix: str = '', replay_path: str = No
         else:
             if not os.path.exists(replay_path):
                 os.makedirs(replay_path)
-            filename = replay_path+f'/connect4_{replay_name_suffix}.{format}'
+            filename = replay_path + f'/connect4_{replay_name_suffix}.{format}'
 
         if format == 'gif':
             # Save frames as a GIF with a duration of 0.1 seconds per frame.

zoo/board_games/gomoku/envs/gomoku_env.py

@@ -54,8 +54,8 @@ class GomokuEnv(BaseEnv):
         # (str) The render mode. Options are 'None', 'state_realtime_mode', 'image_realtime_mode' or 'image_savefile_mode'.
         # If None, then the game will not be rendered.
         render_mode=None,
-        # (str) The suffix of the replay name.
-        replay_path='./video',
+        # (str or None) The directory in which to save the replay file. If None, the file is saved in the current directory.
+        replay_path=None,
         # (float) The scale of the render screen.
         screen_scaling=9,
         # (bool) Whether to use the 'channel last' format for the observation space. If False, 'channel first' format is used.

zoo/board_games/tictactoe/envs/tictactoe_env.py

@@ -38,17 +38,33 @@ def _get_done_winner_func_lru(board_tuple):
 
 @ENV_REGISTRY.register('tictactoe')
 class TicTacToeEnv(BaseEnv):
+
     config = dict(
+        # env_name (str): The name of the environment.
         env_name="TicTacToe",
+        # battle_mode (str): The mode of the battle. Choices are 'self_play_mode' or 'alpha_beta_pruning'.
         battle_mode='self_play_mode',
-        mcts_mode='self_play_mode',  # only used in AlphaZero
-        bot_action_type='v0',  # {'v0', 'alpha_beta_pruning'}
+        # mcts_mode (str): The mode of Monte Carlo Tree Search. This is only used in AlphaZero.
+        mcts_mode='self_play_mode',
+        # bot_action_type (str): The type of action the bot should take. Choices are 'v0' or 'alpha_beta_pruning'.
+        bot_action_type='v0',
+        # save_replay_gif (bool): If True, the replay will be saved as a gif file.
+        save_replay_gif=False,
+        # replay_path_gif (str): The path to save the replay gif.
+        replay_path_gif='./replay_gif',
+        # agent_vs_human (bool): If True, the agent will play against a human.
         agent_vs_human=False,
+        # prob_random_agent (int): The probability of the random agent.
         prob_random_agent=0,
+        # prob_expert_agent (int): The probability of the expert agent.
         prob_expert_agent=0,
+        # channel_last (bool): If True, the channel will be the last dimension.
         channel_last=True,
+        # scale (bool): If True, the pixel values will be scaled.
         scale=True,
+        # stop_value (int): The value to stop the game.
         stop_value=1,
+        # alphazero_mcts_ctree (bool): If True, the Monte Carlo Tree Search from AlphaZero is used.
         alphazero_mcts_ctree=False,
     )
 

zoo/box2d/lunarlander/envs/lunarlander_env.py

@@ -30,7 +30,8 @@ class LunarLanderEnv(CartPoleEnv):
         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,
-        # (str or None) The path to save the replay. If None, the replay will not be saved.
+        # replay_path (str or None): The path to save the replay video. If None, the replay will not be saved.
+        # Only effective when env_manager.type is 'base'.
         replay_path=None,
         # (bool) If True, the action will be scaled.
         act_scale=True,

zoo/classic_control/cartpole/envs/cartpole_lightzero_env.py

@@ -1,3 +1,4 @@
+import copy
 from datetime import datetime
 from typing import Union, Optional, Dict
 
@@ -7,6 +8,7 @@
 from ding.envs import ObsPlusPrevActRewWrapper
 from ding.torch_utils import to_ndarray
 from ding.utils import ENV_REGISTRY
+from easydict import EasyDict
 
 
 @ENV_REGISTRY.register('cartpole_lightzero')
@@ -18,6 +20,20 @@ class CartPoleEnv(BaseEnv):
     environment.
     """
 
+    config = dict(
+        # env_name (str): The name of the environment.
+        env_name="CartPole-v0",
+        # replay_path (str): The path to save the replay video. If None, the replay will not be saved.
+        # Only effective when env_manager.type is 'base'.
+        replay_path=None,
+    )
+
+    @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:
         """
         Initialize the environment with a configuration dictionary. Sets up spaces for observations, actions, and rewards.

zoo/classic_control/pendulum/envs/pendulum_lightzero_env.py

@@ -31,7 +31,8 @@ def default_config(cls: type) -> EasyDict:
     config = dict(
         # (bool) Whether to use continuous action space
         continuous=True,
-        # (str) The path to save replay videos
+        # replay_path (str or None): The path to save the replay video. If None, the replay will not be saved.
+        # Only effective when env_manager.type is 'base'.
         replay_path=None,
         # (bool) Whether to scale action into [-2, 2]
         act_scale=True,

zoo/game_2048/envs/game_2048_env.py

@@ -147,7 +147,7 @@ def __init__(self, cfg: dict) -> None:
         self.replay_format = cfg.replay_format
         self.replay_name_suffix = cfg.replay_name_suffix
         self.replay_path = cfg.replay_path
-        self.render_mode = cfg.render_mode 
+        self.render_mode = cfg.render_mode
 
         self.channel_last = cfg.channel_last
         self.obs_type = cfg.obs_type
@@ -356,8 +356,8 @@ def step(self, action):
         if done:
             info['eval_episode_return'] = self._final_eval_reward
             if self.render_mode == 'image_savefile_mode':
-                    self.save_render_output(replay_name_suffix=self.replay_name_suffix, replay_path=self.replay_path,
-                                            format=self.replay_format)
+                self.save_render_output(replay_name_suffix=self.replay_name_suffix, replay_path=self.replay_path,
+                                        format=self.replay_format)
 
         return BaseEnvTimestep(observation, reward, done, info)
 
@@ -718,7 +718,7 @@ def save_render_output(self, replay_name_suffix: str = '', replay_path=None, for
         else:
             if not os.path.exists(replay_path):
                 os.makedirs(replay_path)
-            filename = replay_path+f'/2048_{replay_name_suffix}.{format}'
+            filename = replay_path + f'/2048_{replay_name_suffix}.{format}'
 
         if format == 'gif':
             imageio.mimsave(filename, self.frames, 'GIF')