fix(nyz): fix enable_save_figure env interface

ding/envs/env_manager/base_env_manager.py

@@ -66,10 +66,10 @@ class BaseEnvManager(object):
     Overview:
         Create a BaseEnvManager to manage multiple environments.
     Interfaces:
-        reset, step, seed, close, enable_save_replay, launch, default_config, env_state_done
+        reset, step, seed, close, enable_save_replay, launch, default_config, env_state_done, reward_shaping, \
+        enable_save_figure
     Properties:
-        env_num, ready_obs, done, method_name_list
-        observation_space, action_space, reward_space
+        env_num, ready_obs, done, method_name_list, observation_space, action_space, reward_space
     """
 
     @classmethod
@@ -411,7 +411,7 @@ def seed(self, seed: Union[Dict[int, int], List[int], int], dynamic_seed: bool =
     def enable_save_replay(self, replay_path: Union[List[str], str]) -> None:
         """
         Overview:
-            Set each env's replay save path.
+            Enable all environments to save replay video after each episode terminates.
         Arguments:
             - replay_path (:obj:`Union[List[str], str]`): List of paths for each environment; \
                 Or one path for all environments.
@@ -420,18 +420,15 @@ def enable_save_replay(self, replay_path: Union[List[str], str]) -> None:
             replay_path = [replay_path] * self.env_num
         self._env_replay_path = replay_path
 
-    def enable_save_figure(self, env_id: int, figure_path: Union[List[str], str]) -> None:
+    def enable_save_figure(self, env_id: int, figure_path: str) -> None:
         """
         Overview:
-            Set each env's replay save path.
+            Enable a specific env to save figure (e.g. environment statistics or episode return curve).
         Arguments:
-            - replay_path (:obj:`Union[List[str], str]`): List of paths for each environment; \
-                Or one path for all environments.
+            - figure_path (:obj:`str`): The file directory path for all environments to save figures.
         """
-        if isinstance(figure_path, str):
-            self._env[env_id].enable_save_figure(figure_path)
-        else:
-            raise TypeError("invalid figure_path arguments type: {}".format(type(figure_path)))
+        assert figure_path is not None
+        self._env[env_id].enable_save_figure(figure_path)
 
     def close(self) -> None:
         """

ding/worker/collector/interaction_serial_evaluator.py

@@ -25,10 +25,12 @@ class InteractionSerialEvaluator(ISerialEvaluator):
         # Evaluate every "eval_freq" training iterations.
         eval_freq=1000,
         render=dict(
-            # tensorboard video render is disabled by default
+            # Tensorboard video render is disabled by default.
             render_freq=-1,
             mode='train_iter',
-        )
+        ),
+        # File path for visualize environment information.
+        figure_path=None,
     )
 
     def __init__(
@@ -231,9 +233,8 @@ def eval(
                             continue
                         if t.done:
                             # Env reset is done by env_manager automatically.
-                            if 'figure_path' in self._cfg:
-                                if self._cfg.figure_path is not None:
-                                    self._env.enable_save_figure(env_id, self._cfg.figure_path)
+                            if 'figure_path' in self._cfg and self._cfg.figure_path is not None:
+                                self._env.enable_save_figure(env_id, self._cfg.figure_path)
                             self._policy.reset([env_id])
                             reward = t.info['eval_episode_return']
                             if 'episode_info' in t.info: