[rllib] Fix Preprocessor for ATARI (#1066)

* Removing squeeze, fix atari preprocessing

* nit comment

* comments

* jenkins

* Lint

python/ray/rllib/a3c/envs.py

@@ -30,7 +30,7 @@ class RLLibPreprocessing(gym.ObservationWrapper):
         self.observation_space = Box(-1.0, 1.0, self._process_shape)
 
     def _observation(self, observation):
-        return self.preprocessor.transform(observation).squeeze(0)
+        return self.preprocessor.transform(observation)
 
 
 class Diagnostic(gym.Wrapper):

python/ray/rllib/es/policies.py

@@ -99,15 +99,13 @@ class Policy:
         t = 0
         if save_obs:
             obs = []
-        # TODO(ekl) the squeeze() is needed for Pong-v0, but we should fix
+        ob = preprocessor.transform(env.reset())
-        # this in the preprocessor instead
-        ob = preprocessor.transform(env.reset()).squeeze()
         for _ in range(timestep_limit):
             ac = self.act(ob[None], random_stream=random_stream)[0]
             if save_obs:
                 obs.append(ob)
             ob, rew, done, _ = env.step(ac)
-            ob = preprocessor.transform(ob).squeeze()
+            ob = preprocessor.transform(ob)
             rews.append(rew)
             t += 1
             if render:

python/ray/rllib/models/preprocessors.py

@@ -42,12 +42,15 @@ class AtariPixelPreprocessor(Preprocessor):
         scaled = observation[25:-25, :, :]
         if self.dim < 80:
             scaled = cv2.resize(scaled, (80, 80))
+        # OpenAI: Resize by half, then down to 42x42 (essentially mipmapping).
+        # If we resize directly we lose pixels that, when mapped to 42x42,
+        # aren't close enough to the pixel boundary.
         scaled = cv2.resize(scaled, (self.dim, self.dim))
         if self.grayscale:
             scaled = scaled.mean(2)
             scaled = scaled.astype(np.float32)
+            # Rescale needed for maintaining 1 channel
             scaled = np.reshape(scaled, [self.dim, self.dim, 1])
-        scaled = scaled[None]
         if self.zero_mean:
             scaled = (scaled - 128) / 128
         else:

python/ray/rllib/ppo/env.py

@@ -22,7 +22,8 @@ class BatchedEnv(object):
 
     def reset(self):
         observations = [
-            self.preprocessor.transform(env.reset()) for env in self.envs]
+            self.preprocessor.transform(env.reset())[None]
+            for env in self.envs]
         self.shape = observations[0].shape
         self.dones = [False for _ in range(self.batchsize)]
         return np.vstack(observations)
@@ -43,7 +44,7 @@ class BatchedEnv(object):
                     break
             if render:
                 self.envs[0].render()
-            observations.append(self.preprocessor.transform(observation))
+            observations.append(self.preprocessor.transform(observation)[None])
             rewards.append(reward)
             self.dones[i] = done
         return (np.vstack(observations), np.array(rewards, dtype="float32"),

test/jenkins_tests/run_multi_node_tests.sh

@@ -84,6 +84,13 @@ docker run --shm-size=10G --memory=10G $DOCKER_SHA \
     --num-iterations 2 \
     --config '{"stepsize": 0.01}'
 
+docker run --shm-size=10G --memory=10G $DOCKER_SHA \
+    python /ray/python/ray/rllib/train.py \
+    --env CartPole-v0 \
+    --alg A3C \
+    --num-iterations 2 \
+    --config '{"use_lstm": false}'
+
 docker run --shm-size=10G --memory=10G $DOCKER_SHA \
     python /ray/python/ray/rllib/train.py \
     --env CartPole-v0 \