Separate send environment data from reset

ml-agents/mlagents/trainers/env_manager.py

@@ -67,6 +67,15 @@ def reset(self, config: Dict = None) -> int:
         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

@@ -205,7 +205,7 @@ def maybe_add_samplers(
         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

@@ -43,16 +43,24 @@ def _step(self) -> List[EnvironmentStep]:
     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_params.set_float_parameter(k, v)
                 elif isinstance(v, ParameterRandomizationSettings):
                     v.apply(k, self.env_params)
-        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

@@ -46,7 +46,7 @@
 class EnvironmentCommand(enum.Enum):
     STEP = 1
     EXTERNAL_BRAINS = 2
-    GET_PROPERTIES = 3
+    ENVIRONMENT_PARAMETERS = 3
     RESET = 4
     CLOSE = 5
     ENV_EXITED = 6
@@ -174,12 +174,13 @@ def external_brains():
                 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:
                 for k, v in req.payload.items():
                     if isinstance(v, float):
                         env_parameters.set_float_parameter(k, v)
                     elif isinstance(v, ParameterRandomizationSettings):
                         v.apply(k, env_parameters)
+            elif req.cmd == EnvironmentCommand.RESET:
                 env.reset()
                 all_step_result = _generate_all_results()
                 _send_response(EnvironmentCommand.RESET, all_step_result)
@@ -291,6 +292,8 @@ def _reset_env(self, config: Optional[Dict] = None) -> List[EnvironmentStep]:
             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)
@@ -299,6 +302,15 @@ def _reset_env(self, config: Optional[Dict] = None) -> List[EnvironmentStep]:
             ew.previous_step = EnvironmentStep(ew.recv().payload, ew.worker_id, {}, {})
         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]:
         self.env_workers[0].send(EnvironmentCommand.EXTERNAL_BRAINS)