add defaults to ActionTuple constructor

ml-agents-envs/mlagents_envs/base_env.py

     respectively.
     """
 
-    def __init__(self, continuous: np.ndarray, discrete: np.ndarray):
-        if continuous.dtype != np.float32:
+    def __init__(
+        self,
+        continuous: Optional[np.ndarray] = None,
+        discrete: Optional[np.ndarray] = None,
+    ):
+        if continuous is not None and continuous.dtype != np.float32:
-        if discrete.dtype != np.int32:
+        if discrete is not None and discrete.dtype != np.int32:
             discrete = discrete.astype(np.int32, copy=False)
         self._discrete = discrete
 

ml-agents/mlagents/trainers/env_manager.py

         return len(step_infos)
 
     @staticmethod
-    def action_buffers_from_numpy_dict(
-        action_dict: Dict[str, np.ndarray]
-    ) -> ActionTuple:
+    def action_tuple_from_numpy_dict(action_dict: Dict[str, np.ndarray]) -> ActionTuple:
         continuous: np.ndarray = np.array([], dtype=np.float32)
         discrete: np.ndarray = np.array([], dtype=np.int32)
         if "continuous_action" in action_dict:

ml-agents/mlagents/trainers/simple_env_manager.py

         self.previous_all_action_info = all_action_info
 
         for brain_name, action_info in all_action_info.items():
-            _action = EnvManager.action_buffers_from_numpy_dict(action_info.action)
+            _action = EnvManager.action_tuple_from_numpy_dict(action_info.action)
             self.env.set_actions(brain_name, _action)
         self.env.step()
         all_step_result = self._generate_all_results()

ml-agents/mlagents/trainers/subprocess_env_manager.py

                 all_action_info = req.payload
                 for brain_name, action_info in all_action_info.items():
                     if len(action_info.action) != 0:
-                        _action = EnvManager.action_buffers_from_numpy_dict(
+                        _action = EnvManager.action_tuple_from_numpy_dict(
                             action_info.action
                         )
                         env.set_actions(brain_name, _action)