diff --git a/python/ray/rllib/rollout.py b/python/ray/rllib/rollout.py
index 2fd81b12032f..af12e9c594f6 100755
--- a/python/ray/rllib/rollout.py
+++ b/python/ray/rllib/rollout.py
@@ -5,6 +5,7 @@
 from __future__ import print_function
 
 import argparse
+import collections
 import json
 import os
 import pickle
@@ -12,6 +13,8 @@
 import gym
 import ray
 from ray.rllib.agents.registry import get_agent_class
+from ray.rllib.env import MultiAgentEnv
+from ray.rllib.env.base_env import _DUMMY_AGENT_ID
 from ray.rllib.evaluation.sample_batch import DEFAULT_POLICY_ID
 from ray.tune.util import merge_dicts
 
@@ -102,17 +105,35 @@ def run(args, parser):
     rollout(agent, args.env, num_steps, args.out, args.no_render)
 
 
+class DefaultMapping(collections.defaultdict):
+    """default_factory now takes as an argument the missing key."""
+
+    def __missing__(self, key):
+        self[key] = value = self.default_factory(key)
+        return value
+
+
+def default_policy_agent_mapping(unused_agent_id):
+    return DEFAULT_POLICY_ID
+
+
 def rollout(agent, env_name, num_steps, out=None, no_render=True):
+    policy_agent_mapping = default_policy_agent_mapping
+
     if hasattr(agent, "local_evaluator"):
         env = agent.local_evaluator.env
-        multiagent = agent.local_evaluator.multiagent
-        if multiagent:
+        multiagent = isinstance(env, MultiAgentEnv)
+        if agent.local_evaluator.multiagent:
             policy_agent_mapping = agent.config["multiagent"][
                 "policy_mapping_fn"]
-            mapping_cache = {}
+
         policy_map = agent.local_evaluator.policy_map
         state_init = {p: m.get_initial_state() for p, m in policy_map.items()}
         use_lstm = {p: len(s) > 0 for p, s in state_init.items()}
+        action_init = {
+            p: m.action_space.sample()
+            for p, m in policy_map.items()
+        }
     else:
         env = gym.make(env_name)
         multiagent = False
@@ -122,39 +143,50 @@ def rollout(agent, env_name, num_steps, out=None, no_render=True):
         rollouts = []
     steps = 0
     while steps < (num_steps or steps + 1):
+        mapping_cache = {}  # in case policy_agent_mapping is stochastic
         if out is not None:
             rollout = []
-        state = env.reset()
+        obs = env.reset()
+        agent_states = DefaultMapping(
+            lambda agent_id: state_init[mapping_cache[agent_id]])
+        prev_actions = DefaultMapping(
+            lambda agent_id: action_init[mapping_cache[agent_id]])
+        prev_rewards = collections.defaultdict(lambda: 0.)
         done = False
         reward_total = 0.0
         while not done and steps < (num_steps or steps + 1):
+            multi_obs = obs if multiagent else {_DUMMY_AGENT_ID: obs}
+            action_dict = {}
+            for agent_id, a_obs in multi_obs.items():
+                if a_obs is not None:
+                    policy_id = mapping_cache.setdefault(
+                        agent_id, policy_agent_mapping(agent_id))
+                    p_use_lstm = use_lstm[policy_id]
+                    if p_use_lstm:
+                        a_action, p_state, _ = agent.compute_action(
+                            a_obs,
+                            state=agent_states[agent_id],
+                            prev_action=prev_actions[agent_id],
+                            prev_reward=prev_rewards[agent_id],
+                            policy_id=policy_id)
+                        agent_states[agent_id] = p_state
+                    else:
+                        a_action = agent.compute_action(
+                            a_obs,
+                            prev_action=prev_actions[agent_id],
+                            prev_reward=prev_rewards[agent_id],
+                            policy_id=policy_id)
+                    action_dict[agent_id] = a_action
+                    prev_actions[agent_id] = a_action
+            action = action_dict
+
+            action = action if multiagent else action[_DUMMY_AGENT_ID]
+            next_obs, reward, done, _ = env.step(action)
             if multiagent:
-                action_dict = {}
-                for agent_id in state.keys():
-                    a_state = state[agent_id]
-                    if a_state is not None:
-                        policy_id = mapping_cache.setdefault(
-                            agent_id, policy_agent_mapping(agent_id))
-                        p_use_lstm = use_lstm[policy_id]
-                        if p_use_lstm:
-                            a_action, p_state_init, _ = agent.compute_action(
-                                a_state,
-                                state=state_init[policy_id],
-                                policy_id=policy_id)
-                            state_init[policy_id] = p_state_init
-                        else:
-                            a_action = agent.compute_action(
-                                a_state, policy_id=policy_id)
-                        action_dict[agent_id] = a_action
-                action = action_dict
+                for agent_id, r in reward.items():
+                    prev_rewards[agent_id] = r
             else:
-                if use_lstm[DEFAULT_POLICY_ID]:
-                    action, state_init, _ = agent.compute_action(
-                        state, state=state_init)
-                else:
-                    action = agent.compute_action(state)
-
-            next_state, reward, done, _ = env.step(action)
+                prev_rewards[_DUMMY_AGENT_ID] = reward
 
             if multiagent:
                 done = done["__all__"]
@@ -164,9 +196,9 @@ def rollout(agent, env_name, num_steps, out=None, no_render=True):
             if not no_render:
                 env.render()
             if out is not None:
-                rollout.append([state, action, next_state, reward, done])
+                rollout.append([obs, action, next_obs, reward, done])
             steps += 1
-            state = next_state
+            obs = next_obs
         if out is not None:
             rollouts.append(rollout)
         print("Episode reward", reward_total)