Remove from X import Y convention in RLlib ES. (#1774)

python/ray/rllib/agent.py

@@ -220,7 +220,7 @@ class _ParameterTuningAgent(_MockAgent):
 
 
 def get_agent_class(alg):
-    """Returns the class of an known agent given its name."""
+    """Returns the class of a known agent given its name."""
 
     if alg == "PPO":
         from ray.rllib import ppo

python/ray/rllib/es/es.py

@@ -12,14 +12,12 @@ import pickle
 import time
 
 import ray
-from ray.rllib.agent import Agent
-from ray.rllib.models import ModelCatalog
+from ray.rllib import agent
 
 from ray.rllib.es import optimizers
 from ray.rllib.es import policies
 from ray.rllib.es import tabular_logger as tlogger
 from ray.rllib.es import utils
-from ray.tune.result import TrainingResult
 
 
 Result = namedtuple("Result", [
@@ -72,7 +70,9 @@ class Worker(object):
         self.noise = SharedNoiseTable(noise)
 
         self.env = env_creator(config["env_config"])
-        self.preprocessor = ModelCatalog.get_preprocessor(registry, self.env)
+        from ray.rllib import models
+        self.preprocessor = models.ModelCatalog.get_preprocessor(
+            registry, self.env)
 
         self.sess = utils.make_session(single_threaded=True)
         self.policy = policies.GenericPolicy(
@@ -133,7 +133,7 @@ class Worker(object):
             eval_lengths=eval_lengths)
 
 
-class ESAgent(Agent):
+class ESAgent(agent.Agent):
     _agent_name = "ES"
     _default_config = DEFAULT_CONFIG
     _allow_unknown_subkeys = ["env_config"]
@@ -144,7 +144,9 @@ class ESAgent(Agent):
         }
 
         env = self.env_creator(self.config["env_config"])
-        preprocessor = ModelCatalog.get_preprocessor(self.registry, env)
+        from ray.rllib import models
+        preprocessor = models.ModelCatalog.get_preprocessor(
+            self.registry, env)
 
         self.sess = utils.make_session(single_threaded=False)
         self.policy = policies.GenericPolicy(
@@ -292,7 +294,7 @@ class ESAgent(Agent):
             "time_elapsed": step_tend - self.tstart
         }
 
-        result = TrainingResult(
+        result = ray.tune.result.TrainingResult(
             episode_reward_mean=eval_returns.mean(),
             episode_len_mean=eval_lengths.mean(),
             timesteps_this_iter=noisy_lengths.sum(),