Merge branch 'master' into develop-action-buffer

README.md

 
 **The Unity Machine Learning Agents Toolkit** (ML-Agents) is an open-source
 project that enables games and simulations to serve as environments for
-training intelligent agents. Agents can be trained using reinforcement learning,
-imitation learning, neuroevolution, or other machine learning methods through a
-simple-to-use Python API. We also provide implementations (based on PyTorch)
+training intelligent agents. We provide implementations (based on PyTorch)
-train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be
+train intelligent agents for 2D, 3D and VR/AR games. Researchers can also use the
+provided simple-to-use Python API to train Agents using reinforcement learning,
+imitation learning, neuroevolution, or any other methods. These trained agents can be
 used for multiple purposes, including controlling NPC behavior (in a variety of
 settings such as multi-agent and adversarial), automated testing of game builds
 and evaluating different game design decisions pre-release. The ML-Agents

ml-agents-envs/mlagents_envs/base_env.py

         if actions.discrete.dtype != np.int32:
             actions.discrete = actions.discrete.astype(np.int32)
 
-        return actions
+        return action
 
     @staticmethod
     def create_continuous(continuous_size: int) -> "ActionSpec":

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

         :param num_agents: Number of agents.
         :return: ActionBuffers .
         """
-        return self.behavior_spec.action_spec.create_empty(num_agents)
+        return self.behavior_spec.action_spec.empty_action(num_agents)
 
     def save_previous_action(
         self, agent_ids: List[str], action_buffers: Optional[ActionBuffers]