[rllib] Moved clip_action into policy_graph; Clip actions in compute_single_action

python/ray/rllib/agents/agent.py

@@ -437,9 +437,19 @@ def compute_action(self,
             preprocessed, update=False)
         if state:
             return self.get_policy(policy_id).compute_single_action(
-                filtered_obs, state, prev_action, prev_reward, info)
+                filtered_obs,
+                state,
+                prev_action,
+                prev_reward,
+                info,
+                clip_actions=self.config["clip_actions"])
         return self.get_policy(policy_id).compute_single_action(
-            filtered_obs, state, prev_action, prev_reward, info)[0]
+            filtered_obs,
+            state,
+            prev_action,
+            prev_reward,
+            info,
+            clip_actions=self.config["clip_actions"])[0]
 
     @property
     def iteration(self):
@@ -657,12 +667,12 @@ def session_creator():
             input_creator = (lambda ioctx: ioctx.default_sampler_input())
         elif isinstance(config["input"], dict):
             input_creator = (lambda ioctx: ShuffledInput(
-                MixedInput(config["input"], ioctx),
-                config["shuffle_buffer_size"]))
+                MixedInput(config["input"], ioctx), config[
+                    "shuffle_buffer_size"]))
         else:
             input_creator = (lambda ioctx: ShuffledInput(
-                JsonReader(config["input"], ioctx),
-                config["shuffle_buffer_size"]))
+                JsonReader(config["input"], ioctx), config[
+                    "shuffle_buffer_size"]))
 
         if isinstance(config["output"], FunctionType):
             output_creator = config["output"]

python/ray/rllib/evaluation/policy_graph.py

@@ -2,6 +2,9 @@
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
+import gym
+
 from ray.rllib.utils.annotations import DeveloperAPI
 
 
@@ -81,6 +84,7 @@ def compute_single_action(self,
                               prev_reward=None,
                               info=None,
                               episode=None,
+                              clip_actions=False,
                               **kwargs):
         """Unbatched version of compute_actions.
 
@@ -93,6 +97,7 @@ def compute_single_action(self,
             episode (MultiAgentEpisode): this provides access to all of the
                 internal episode state, which may be useful for model-based or
                 multi-agent algorithms.
+            clip_actions (bool): should the action be clipped
             kwargs: forward compatibility placeholder
 
         Returns:
@@ -119,6 +124,8 @@ def compute_single_action(self,
             prev_reward_batch=prev_reward_batch,
             info_batch=info_batch,
             episodes=episodes)
+        if clip_actions:
+            action = clip_action(action, self.action_space)
         return action, [s[0] for s in state_out], \
             {k: v[0] for k, v in info.items()}
 
@@ -263,3 +270,28 @@ def export_checkpoint(self, export_dir):
             export_dir (str): Local writable directory.
         """
         raise NotImplementedError
+
+
+def clip_action(action, space):
+    """Called to clip actions to the specified range of this policy.
+
+    Arguments:
+        action: Single action.
+        space: Action space the actions should be present in.
+
+    Returns:
+        Clipped batch of actions.
+    """
+
+    if isinstance(space, gym.spaces.Box):
+        return np.clip(action, space.low, space.high)
+    elif isinstance(space, gym.spaces.Tuple):
+        if type(action) not in (tuple, list):
+            raise ValueError("Expected tuple space for actions {}: {}".format(
+                action, space))
+        out = []
+        for a, s in zip(action, space.spaces):
+            out.append(clip_action(a, s))
+        return out
+    else:
+        return action

python/ray/rllib/evaluation/sampler.py

@@ -2,7 +2,6 @@
 from __future__ import division
 from __future__ import print_function
 
-import gym
 from collections import defaultdict, namedtuple
 import logging
 import numpy as np
@@ -21,6 +20,7 @@
 from ray.rllib.utils.annotations import override
 from ray.rllib.utils.debug import log_once, summarize
 from ray.rllib.utils.tf_run_builder import TFRunBuilder
+from ray.rllib.evaluation.policy_graph import clip_action
 
 logger = logging.getLogger(__name__)
 
@@ -224,31 +224,6 @@ def get_extra_batches(self):
         return extra
 
 
-def clip_action(action, space):
-    """Called to clip actions to the specified range of this policy.
-
-    Arguments:
-        action: Single action.
-        space: Action space the actions should be present in.
-
-    Returns:
-        Clipped batch of actions.
-    """
-
-    if isinstance(space, gym.spaces.Box):
-        return np.clip(action, space.low, space.high)
-    elif isinstance(space, gym.spaces.Tuple):
-        if type(action) not in (tuple, list):
-            raise ValueError("Expected tuple space for actions {}: {}".format(
-                action, space))
-        out = []
-        for a, s in zip(action, space.spaces):
-            out.append(clip_action(a, s))
-        return out
-    else:
-        return action
-
-
 def _env_runner(base_env, extra_batch_callback, policies, policy_mapping_fn,
                 unroll_length, horizon, preprocessors, obs_filters,
                 clip_rewards, clip_actions, pack, callbacks, tf_sess,

python/ray/rllib/rollout.py

@@ -13,7 +13,6 @@
 import ray
 from ray.rllib.agents.registry import get_agent_class
 from ray.rllib.evaluation.sample_batch import DEFAULT_POLICY_ID
-from ray.rllib.evaluation.sampler import clip_action
 from ray.tune.util import merge_dicts
 
 EXAMPLE_USAGE = """
@@ -155,11 +154,7 @@ def rollout(agent, env_name, num_steps, out=None, no_render=True):
                 else:
                     action = agent.compute_action(state)
 
-            if agent.config["clip_actions"]:
-                clipped_action = clip_action(action, env.action_space)
-                next_state, reward, done, _ = env.step(clipped_action)
-            else:
-                next_state, reward, done, _ = env.step(action)
+            next_state, reward, done, _ = env.step(action)
 
             if multiagent:
                 done = done["__all__"]