[rllib] Misc fixes, A2C (#2679)

A bunch of minor rllib fixes:

pull in latest baselines atari wrapper changes (and use deepmind wrapper by default)
move reward clipping to policy evaluator
add a2c variant of a3c
reduce vision network fc layer size to 256 units
switch to 84x84 images
doc tweaks
print timesteps in tune status

python/ray/rllib/optimizers/sync_replay_optimizer.py

@@ -31,8 +31,7 @@ class SyncReplayOptimizer(PolicyOptimizer):
               prioritized_replay_beta=0.4,
               prioritized_replay_eps=1e-6,
               train_batch_size=32,
-              sample_batch_size=4,
-              clip_rewards=True):
+              sample_batch_size=4):
 
         self.replay_starts = learning_starts
         self.prioritized_replay_beta = prioritized_replay_beta
@@ -51,13 +50,11 @@ class SyncReplayOptimizer(PolicyOptimizer):
 
             def new_buffer():
                 return PrioritizedReplayBuffer(
-                    buffer_size,
-                    alpha=prioritized_replay_alpha,
-                    clip_rewards=clip_rewards)
+                    buffer_size, alpha=prioritized_replay_alpha)
         else:
 
             def new_buffer():
-                return ReplayBuffer(buffer_size, clip_rewards)
+                return ReplayBuffer(buffer_size)
 
         self.replay_buffers = collections.defaultdict(new_buffer)