diff --git a/python/ray/rllib/rollout.py b/python/ray/rllib/rollout.py
old mode 100644
new mode 100755
index 324903d91..64174866a
--- 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.agent import get_agent_class
+from ray.rllib.dqn.common.wrappers import wrap_dqn
 from ray.rllib.models import ModelCatalog
 from ray.tune.registry import get_registry
 
@@ -67,12 +68,16 @@ if __name__ == "__main__":
     ray.init()
 
     cls = get_agent_class(args.run)
-    agent = cls(env=args.env)
+    agent = cls(env=args.env, config=args.config)
     agent.restore(args.checkpoint)
     num_steps = int(args.steps)
 
-    env = ModelCatalog.get_preprocessor_as_wrapper(get_registry(),
-                                                   gym.make(args.env))
+    if args.run == "DQN":
+        env = gym.make(args.env)
+        env = wrap_dqn(get_registry(), env, args.config.get("model", {}))
+    else:
+        env = ModelCatalog.get_preprocessor_as_wrapper(get_registry(),
+                                                       gym.make(args.env))
     if args.out is not None:
         rollouts = []
     steps = 0
@@ -81,15 +86,19 @@ if __name__ == "__main__":
             rollout = []
         state = env.reset()
         done = False
+        reward_total = 0.0
         while not done and steps < (num_steps or steps + 1):
             action = agent.compute_action(state)
             next_state, reward, done, _ = env.step(action)
+            reward_total += reward
             if not args.no_render:
                 env.render()
             if args.out is not None:
                 rollout.append([state, action, next_state, reward, done])
             steps += 1
+            state = next_state
         if args.out is not None:
             rollouts.append(rollout)
+        print("Episode reward", reward_total)
     if args.out is not None:
         pickle.dump(rollouts, open(args.out, "wb"))