Hotfix v0.5.0a

.gitignore

@@ -68,7 +68,6 @@
 *.idea/misc.xml
 *.idea/modules.xml
 *.iml
-*.xml
 *.cache
 */build/
 */dist/

UnitySDK/Assets/ML-Agents/Examples/BananaCollectors/Scripts/BananaAgent.cs

@@ -5,8 +5,7 @@
 
 public class BananaAgent : Agent
 {
-    public GameObject myAcademyObj;
-    BananaAcademy myAcademy;
+    private BananaAcademy myAcademy;
     public GameObject area;
     BananaArea myArea;
     bool frozen;
@@ -39,7 +38,7 @@ public override void InitializeAgent()
         Monitor.verticalOffset = 1f;
         myArea = area.GetComponent<BananaArea>();
         rayPer = GetComponent<RayPerception>();
-        myAcademy = myAcademyObj.GetComponent<BananaAcademy>();
+        myAcademy = FindObjectOfType<BananaAcademy>();
     }
 
     public override void CollectObservations()

docs/Python-API.md

@@ -50,7 +50,7 @@ file, put the file in the same directory as `envs`. For example, if the filename
 of your Unity environment is 3DBall.app, in python, run:
 
 ```python
-from mlagents.env import UnityEnvironment
+from mlagents.envs import UnityEnvironment
 env = UnityEnvironment(file_name="3DBall", worker_id=0, seed=1)
 ```
 

gym-unity/gym_unity/envs/unity_env.py

@@ -1,7 +1,8 @@
+import logging
 import gym
 import numpy as np
 from mlagents.envs import UnityEnvironment
-from gym import error, spaces, logger
+from gym import error, spaces
 
 
 class UnityGymException(error.Error):
@@ -11,6 +12,10 @@ class UnityGymException(error.Error):
     pass
 
 
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger("gym_unity")
+
+
 class UnityEnv(gym.Env):
     """
     Provides Gym wrapper for Unity Learning Environments.
@@ -44,7 +49,11 @@ def __init__(self, environment_filename: str, worker_id=0, use_visual=False, mul
         if use_visual and brain.number_visual_observations == 0:
             raise UnityGymException("`use_visual` was set to True, however there are no"
                                     " visual observations as part of this environment.")
-        self.use_visual = brain.number_visual_observations == 1 and use_visual
+        self.use_visual = brain.number_visual_observations >= 1 and use_visual
+
+        if brain.number_visual_observations > 1:
+            logger.warning("The environment contains more than one visual observation. "
+                           "Please note that only the first will be provided in the observation.")
 
         if brain.num_stacked_vector_observations != 1:
             raise UnityGymException(
@@ -114,7 +123,8 @@ def step(self, action):
             if not isinstance(action, list):
                 raise UnityGymException("The environment was expecting `action` to be a list.")
             if len(action) != self._n_agents:
-                raise UnityGymException("The environment was expecting a list of {} actions.".format(self._n_agents))
+                raise UnityGymException(
+                    "The environment was expecting a list of {} actions.".format(self._n_agents))
             else:
                 action = np.array(action)
 
@@ -136,17 +146,19 @@ def _single_step(self, info):
         else:
             default_observation = info.vector_observations[0, :]
 
-        return default_observation, info.rewards[0], info.local_done[0], {"text_observation": info.text_observations[0],
-                                                                          "brain_info": info}
+        return default_observation, info.rewards[0], info.local_done[0], {
+            "text_observation": info.text_observations[0],
+            "brain_info": info}
 
     def _multi_step(self, info):
         if self.use_visual:
             self.visual_obs = info.visual_observations
             default_observation = self.visual_obs
         else:
             default_observation = info.vector_observations
-        return list(default_observation), info.rewards,  info.local_done, {"text_observation": info.text_observations,
-                                                                           "brain_info": info}
+        return list(default_observation), info.rewards, info.local_done, {
+            "text_observation": info.text_observations,
+            "brain_info": info}
 
     def render(self, mode='rgb_array'):
         return self.visual_obs
@@ -170,11 +182,13 @@ def seed(self, seed=None):
 
     def _check_agents(self, n_agents):
         if not self._multiagent and n_agents > 1:
-            raise UnityGymException("The environment was launched as a single-agent environment, however"
-                                    "there is more than one agent in the scene.")
+            raise UnityGymException(
+                "The environment was launched as a single-agent environment, however"
+                "there is more than one agent in the scene.")
         elif self._multiagent and n_agents <= 1:
-            raise UnityGymException("The environment was launched as a mutli-agent environment, however"
-                                    "there is only one agent in the scene.")
+            raise UnityGymException(
+                "The environment was launched as a mutli-agent environment, however"
+                "there is only one agent in the scene.")
         if self._n_agents is None:
             self._n_agents = n_agents
             logger.info("{} agents within environment.".format(n_agents))

gym-unity/setup.py

@@ -1,14 +1,14 @@
 #!/usr/bin/env python
 
-from setuptools import setup, Command, find_packages
+from setuptools import setup, find_packages
 
 setup(name='gym_unity',
-      version='0.1.0',
+      version='0.1.1',
       description='Unity Machine Learning Agents Gym Interface',
       license='Apache License 2.0',
       author='Unity Technologies',
       author_email='[email protected]',
       url='https://github.com/Unity-Technologies/ml-agents',
       packages=find_packages(),
-      install_requires = ['gym', 'mlagents']
-     )
+      install_requires=['gym', 'mlagents']
+      )