[rllib] Make observation filter optional (#940)

* make observation filter optional

* fix linting

python/ray/rllib/ppo/ppo.py

@@ -55,6 +55,8 @@ DEFAULT_CONFIG = {
     "kl_target": 0.01,
     # Config params to pass to the model
     "model": {"free_log_std": False},
+    # Which observation filter to apply to the observation
+    "observation_filter": "MeanStdFilter",
     # If >1, adds frameskip
     "extra_frameskip": 1,
     # Number of timesteps collected in each outer loop

python/ray/rllib/ppo/runner.py

@@ -16,7 +16,7 @@ from ray.rllib.parallel import LocalSyncParallelOptimizer
 from ray.rllib.models import ModelCatalog
 from ray.rllib.ppo.env import BatchedEnv
 from ray.rllib.ppo.loss import ProximalPolicyLoss
-from ray.rllib.ppo.filter import MeanStdFilter
+from ray.rllib.ppo.filter import NoFilter, MeanStdFilter
 from ray.rllib.ppo.rollout import (
     rollouts, add_return_values, add_advantage_values)
 from ray.rllib.ppo.utils import flatten, concatenate
@@ -140,8 +140,14 @@ class Runner(object):
         self.common_policy = self.par_opt.get_common_loss()
         self.variables = ray.experimental.TensorFlowVariables(
             self.common_policy.loss, self.sess)
-        self.observation_filter = MeanStdFilter(
-            self.preprocessor_shape, clip=None)
+        if config["observation_filter"] == "MeanStdFilter":
+            self.observation_filter = MeanStdFilter(
+                self.preprocessor_shape, clip=None)
+        elif config["observation_filter"] == "NoFilter":
+            self.observation_filter = NoFilter()
+        else:
+            raise Exception("Unknown observation_filter: " +
+                            str(config["observation_filter"]))
         self.reward_filter = MeanStdFilter((), clip=5.0)
         self.sess.run(tf.global_variables_initializer())