diff --git a/python/ray/rllib/evaluation/sampler.py b/python/ray/rllib/evaluation/sampler.py
index 0c3032178..cd6935f8a 100644
--- a/python/ray/rllib/evaluation/sampler.py
+++ b/python/ray/rllib/evaluation/sampler.py
@@ -200,6 +200,31 @@ class AsyncSampler(threading.Thread, SamplerInput):
         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,
@@ -526,7 +551,7 @@ def _process_policy_eval_results(to_eval, eval_results, active_episodes,
             env_id = eval_data[i].env_id
             agent_id = eval_data[i].agent_id
             if clip_actions:
-                actions_to_send[env_id][agent_id] = _clip_actions(
+                actions_to_send[env_id][agent_id] = clip_action(
                     action, policy.action_space)
             else:
                 actions_to_send[env_id][agent_id] = action
@@ -563,31 +588,6 @@ def _fetch_atari_metrics(base_env):
     return atari_out
 
 
-def _clip_actions(actions, space):
-    """Called to clip actions to the specified range of this policy.
-
-    Arguments:
-        actions: 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(actions, space.low, space.high)
-    elif isinstance(space, gym.spaces.Tuple):
-        if type(actions) not in (tuple, list):
-            raise ValueError("Expected tuple space for actions {}: {}".format(
-                actions, space))
-        out = []
-        for a, s in zip(actions, space.spaces):
-            out.append(_clip_actions(a, s))
-        return out
-    else:
-        return actions
-
-
 def _unbatch_tuple_actions(action_batch):
     # convert list of batches -> batch of lists
     if isinstance(action_batch, TupleActions):
diff --git a/python/ray/rllib/rollout.py b/python/ray/rllib/rollout.py
index 70b00eb63..0bd364583 100755
--- a/python/ray/rllib/rollout.py
+++ b/python/ray/rllib/rollout.py
@@ -12,6 +12,7 @@ import pickle
 import gym
 import ray
 from ray.rllib.agents.registry import get_agent_class
+from ray.rllib.evaluation.sampler import clip_action
 from ray.tune.util import merge_dicts
 
 EXAMPLE_USAGE = """
@@ -153,7 +154,11 @@ def rollout(agent, env_name, num_steps, out=None, no_render=True):
                 else:
                     action = agent.compute_action(state)
 
-            next_state, reward, done, _ = env.step(action)
+            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)
 
             if multiagent:
                 done = done["__all__"]