Separate send environment data from reset (#4128)

ml-agents/mlagents/trainers/env_manager.py

         self.first_step_infos = self._reset_env(config)
         return len(self.first_step_infos)
 
+    @abstractmethod
+    def set_env_parameters(self, config: Dict = None) -> None:
+        """
+        Sends environment parameter settings to C# via the
+        EnvironmentParametersSidehannel.
+        :param config: Dict of environment parameter keys and values
+        """
+        pass
+
     @property
     @abstractmethod
     def external_brains(self) -> Dict[BehaviorName, BrainParameters]:

ml-agents/mlagents/trainers/learn.py

         for offset, v in enumerate(sampler_config.values()):
             if v.seed == -1:
                 v.seed = run_seed + offset
-        env.reset(config=sampler_config)
+        env.set_env_parameters(config=sampler_config)
 
 
 def try_create_meta_curriculum(

ml-agents/mlagents/trainers/simple_env_manager.py

     def _reset_env(
         self, config: Dict[BehaviorName, float] = None
     ) -> List[EnvironmentStep]:  # type: ignore
+        self.set_env_parameters(config)
+        self.env.reset()
+        all_step_result = self._generate_all_results()
+        self.previous_step = EnvironmentStep(all_step_result, 0, {}, {})
+        return [self.previous_step]
+
+    def set_env_parameters(self, config: Dict = None) -> None:
+        """
+        Sends environment parameter settings to C# via the
+        EnvironmentParametersSidehannel.
+        :param config: Dict of environment parameter keys and values
+        """
         if config is not None:
             for k, v in config.items():
                 if isinstance(v, float):
-        self.env.reset()
-        all_step_result = self._generate_all_results()
-        self.previous_step = EnvironmentStep(all_step_result, 0, {}, {})
-        return [self.previous_step]
 
     @property
     def external_brains(self) -> Dict[BehaviorName, BrainParameters]:

ml-agents/mlagents/trainers/subprocess_env_manager.py

 class EnvironmentCommand(enum.Enum):
     STEP = 1
     EXTERNAL_BRAINS = 2
-    GET_PROPERTIES = 3
+    ENVIRONMENT_PARAMETERS = 3
     RESET = 4
     CLOSE = 5
     ENV_EXITED = 6
                 reset_timers()
             elif req.cmd == EnvironmentCommand.EXTERNAL_BRAINS:
                 _send_response(EnvironmentCommand.EXTERNAL_BRAINS, external_brains())
-            elif req.cmd == EnvironmentCommand.RESET:
+            elif req.cmd == EnvironmentCommand.ENVIRONMENT_PARAMETERS:
+            elif req.cmd == EnvironmentCommand.RESET:
                 env.reset()
                 all_step_result = _generate_all_results()
                 _send_response(EnvironmentCommand.RESET, all_step_result)
             if not self.step_queue.empty():
                 step = self.step_queue.get_nowait()
                 self.env_workers[step.worker_id].waiting = False
+        # Send config to environment
+        self.set_env_parameters(config)
         # First enqueue reset commands for all workers so that they reset in parallel
         for ew in self.env_workers:
             ew.send(EnvironmentCommand.RESET, config)
         return list(map(lambda ew: ew.previous_step, self.env_workers))
+
+    def set_env_parameters(self, config: Dict = None) -> None:
+        """
+        Sends environment parameter settings to C# via the
+        EnvironmentParametersSidehannel for each worker.
+        :param config: Dict of environment parameter keys and values
+        """
+        for ew in self.env_workers:
+            ew.send(EnvironmentCommand.ENVIRONMENT_PARAMETERS, config)
 
     @property
     def external_brains(self) -> Dict[BehaviorName, BrainParameters]: