Merge remote-tracking branch 'origin/develop-critic-optimizer' into develop-critic-optimizer

ml-agents/mlagents/trainers/policy/torch_policy.py

         )  # could be much simpler if TorchPolicy is nn.Module
         self.grads = None
 
-        # reward_signal_configs = trainer_settings.reward_signals
-        # reward_signal_names = [key.value for key, _ in reward_signal_configs.items()]
-
-        ac_class = SimpleActor
-        # if separate_critic:
-        #    ac_class = SimpleActor
-        # else:
-        #    ac_class = SharedActorCritic
-        self.actor = ac_class(
+        self.actor = SimpleActor(
             observation_specs=self.behavior_spec.observation_specs,
             network_settings=trainer_settings.network_settings,
             action_spec=behavior_spec.action_spec,

ml-agents/mlagents/trainers/tests/torch/saver/test_saver.py

     """
     Make sure two policies have the same output for the same input.
     """
-    policy1.actor_critic = policy1.actor_critic.to(default_device())
-    policy2.actor_critic = policy2.actor_critic.to(default_device())
+    policy1.actor = policy1.actor.to(default_device())
+    policy2.actor = policy2.actor.to(default_device())
 
     decision_step, _ = mb.create_steps_from_behavior_spec(
         policy1.behavior_spec, num_agents=1