Merge pull request #258 from huangshiyu13/main

fix arena petting zoo import error

openrl/algorithms/dqn.py

@@ -167,7 +167,9 @@ def prepare_loss(
         )
 
         q_targets = rewards_batch + self.gamma * max_next_q_values * next_masks_batch
-        q_loss = torch.mean(F.mse_loss(q_values, q_targets.detach()))  # 均方误差损失函数
+        q_loss = torch.mean(
+            F.mse_loss(q_values, q_targets.detach())
+        )  # 均方误差损失函数
 
         loss_list.append(q_loss)
 

openrl/algorithms/vdn.py

@@ -211,7 +211,9 @@ def prepare_loss(
         rewards_batch = rewards_batch.reshape(-1, self.n_agent, 1)
         rewards_batch = torch.sum(rewards_batch, dim=1, keepdim=True).view(-1, 1)
         q_targets = rewards_batch + self.gamma * max_next_q_values * next_masks_batch
-        q_loss = torch.mean(F.mse_loss(q_values, q_targets.detach()))  # 均方误差损失函数
+        q_loss = torch.mean(
+            F.mse_loss(q_values, q_targets.detach())
+        )  # 均方误差损失函数
 
         loss_list.append(q_loss)
         return loss_list

openrl/arena/__init__.py

@@ -30,9 +30,11 @@ def make_arena(
     **kwargs,
 ):
     if custom_build_env is None:
+        from openrl.envs import PettingZoo
+
         if (
             env_id in pettingzoo_all_envs
-            or env_id in openrl.envs.PettingZoo.registration.pettingzoo_env_dict.keys()
+            or env_id in PettingZoo.registration.pettingzoo_env_dict.keys()
         ):
             from openrl.envs.PettingZoo import make_PettingZoo_env
 

openrl/envs/mpe/rendering.py

@@ -31,12 +31,10 @@
 except ImportError:
     print(
         "Error occured while running `from pyglet.gl import *`",
-        (
-            "HINT: make sure you have OpenGL install. On Ubuntu, you can run 'apt-get"
-            " install python-opengl'. If you're running on a server, you may need a"
-            " virtual frame buffer; something like this should work: 'xvfb-run -s"
-            ' "-screen 0 1400x900x24" python <your_script.py>\''
-        ),
+        "HINT: make sure you have OpenGL install. On Ubuntu, you can run 'apt-get"
+        " install python-opengl'. If you're running on a server, you may need a"
+        " virtual frame buffer; something like this should work: 'xvfb-run -s"
+        ' "-screen 0 1400x900x24" python <your_script.py>\'',
     )
 
 import math

openrl/envs/snake/snake.py

@@ -674,7 +674,9 @@ class Snake:
     def __init__(self, player_id, board_width, board_height, init_len):
         self.actions = [-2, 2, -1, 1]
         self.actions_name = {-2: "up", 2: "down", -1: "left", 1: "right"}
-        self.direction = random.choice(self.actions)  # 方向[-2,2,-1,1]分别表示[上，下，左，右]
+        self.direction = random.choice(
+            self.actions
+        )  # 方向[-2,2,-1,1]分别表示[上，下，左，右]
         self.board_width = board_width
         self.board_height = board_height
         x = random.randrange(0, board_height)

openrl/envs/vec_env/async_venv.py

@@ -234,10 +234,8 @@ def reset_send(
 
         if self._state != AsyncState.DEFAULT:
             raise AlreadyPendingCallError(
-                (
-                    "Calling `reset_send` while waiting for a pending call to"
-                    f" `{self._state.value}` to complete"
-                ),
+                "Calling `reset_send` while waiting for a pending call to"
+                f" `{self._state.value}` to complete",
                 self._state.value,
             )
 
@@ -329,10 +327,8 @@ def step_send(self, actions: np.ndarray):
         self._assert_is_running()
         if self._state != AsyncState.DEFAULT:
             raise AlreadyPendingCallError(
-                (
-                    "Calling `step_send` while waiting for a pending call to"
-                    f" `{self._state.value}` to complete."
-                ),
+                "Calling `step_send` while waiting for a pending call to"
+                f" `{self._state.value}` to complete.",
                 self._state.value,
             )
 
@@ -342,9 +338,7 @@ def step_send(self, actions: np.ndarray):
             pipe.send(("step", action))
         self._state = AsyncState.WAITING_STEP
 
-    def step_fetch(
-        self, timeout: Optional[Union[int, float]] = None
-    ) -> Union[
+    def step_fetch(self, timeout: Optional[Union[int, float]] = None) -> Union[
         Tuple[Any, NDArray[Any], NDArray[Any], List[Dict[str, Any]]],
         Tuple[Any, NDArray[Any], NDArray[Any], NDArray[Any], List[Dict[str, Any]]],
     ]:
@@ -576,10 +570,8 @@ def call_send(self, name: str, *args, **kwargs):
         self._assert_is_running()
         if self._state != AsyncState.DEFAULT:
             raise AlreadyPendingCallError(
-                (
-                    "Calling `call_send` while waiting "
-                    f"for a pending call to `{self._state.value}` to complete."
-                ),
+                "Calling `call_send` while waiting "
+                f"for a pending call to `{self._state.value}` to complete.",
                 str(self._state.value),
             )
 
@@ -636,10 +628,8 @@ def exec_func_send(self, func: Callable, indices, *args, **kwargs):
         self._assert_is_running()
         if self._state != AsyncState.DEFAULT:
             raise AlreadyPendingCallError(
-                (
-                    "Calling `exec_func_send` while waiting "
-                    f"for a pending call to `{self._state.value}` to complete."
-                ),
+                "Calling `exec_func_send` while waiting "
+                f"for a pending call to `{self._state.value}` to complete.",
                 str(self._state.value),
             )
 
@@ -717,10 +707,8 @@ def set_attr(self, name: str, values: Union[List[Any], Tuple[Any], object]):
 
         if self._state != AsyncState.DEFAULT:
             raise AlreadyPendingCallError(
-                (
-                    "Calling `set_attr` while waiting "
-                    f"for a pending call to `{self._state.value}` to complete."
-                ),
+                "Calling `set_attr` while waiting "
+                f"for a pending call to `{self._state.value}` to complete.",
                 str(self._state.value),
             )
 

openrl/utils/callbacks/checkpoint_callback.py

@@ -72,9 +72,7 @@ def _checkpoint_path(self, checkpoint_type: str = "", extension: str = "") -> st
         """
         return os.path.join(
             self.save_path,
-            (
-                f"{self.name_prefix}_{checkpoint_type}{self.num_time_steps}_steps{'.' if extension else ''}{extension}"
-            ),
+            f"{self.name_prefix}_{checkpoint_type}{self.num_time_steps}_steps{'.' if extension else ''}{extension}",
         )
 
     def _on_step(self) -> bool:

openrl/utils/evaluation.py

@@ -68,12 +68,10 @@ def evaluate_policy(
 
     if not is_monitor_wrapped and warn:
         warnings.warn(
-            (
-                "Evaluation environment is not wrapped with a ``Monitor`` wrapper. This"
-                " may result in reporting modified episode lengths and rewards, if"
-                " other wrappers happen to modify these. Consider wrapping environment"
-                " first with ``Monitor`` wrapper."
-            ),
+            "Evaluation environment is not wrapped with a ``Monitor`` wrapper. This"
+            " may result in reporting modified episode lengths and rewards, if"
+            " other wrappers happen to modify these. Consider wrapping environment"
+            " first with ``Monitor`` wrapper.",
             UserWarning,
         )