diff --git a/examples/rl_pong/driver.py b/examples/rl_pong/driver.py
new file mode 100644
index 000000000..a9328c05a
--- /dev/null
+++ b/examples/rl_pong/driver.py
@@ -0,0 +1,52 @@
+# This code is copied and adapted from Andrej Karpathy's code for learning to
+# play Pong https://gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5.
+
+import numpy as np
+import cPickle as pickle
+import gym
+import ray
+import ray.services as services
+import os
+
+import functions
+
+worker_dir = os.path.dirname(os.path.abspath(__file__))
+worker_path = os.path.join(worker_dir, "worker.py")
+services.start_singlenode_cluster(return_drivers=False, num_objstores=1, num_workers_per_objstore=10, worker_path=worker_path)
+
+# hyperparameters
+H = 200 # number of hidden layer neurons
+batch_size = 10 # every how many episodes to do a param update?
+learning_rate = 1e-4
+decay_rate = 0.99 # decay factor for RMSProp leaky sum of grad^2
+resume = False # resume from previous checkpoint?
+
+running_reward = None
+batch_num = 1
+D = functions.D # input dimensionality: 80x80 grid
+if resume:
+  model = pickle.load(open("save.p", "rb"))
+else:
+  model = {}
+  model["W1"] = np.random.randn(H, D) / np.sqrt(D) # "Xavier" initialization
+  model["W2"] = np.random.randn(H) / np.sqrt(H)
+grad_buffer = {k: np.zeros_like(v) for k, v in model.iteritems()} # update buffers that add up gradients over a batch
+rmsprop_cache = {k: np.zeros_like(v) for k, v in model.iteritems()} # rmsprop memory
+
+while True:
+  modelref = ray.push(model)
+  grads = []
+  for i in range(batch_size):
+    grads.append(functions.compgrad(modelref))
+  for i in range(batch_size):
+    grad = ray.pull(grads[i])
+    for k in model: grad_buffer[k] += grad[0][k] # accumulate grad over batch
+    running_reward = grad[1] if running_reward is None else running_reward * 0.99 + grad[1] * 0.01
+    print "Batch {}. episode reward total was {}. running mean: {}".format(batch_num, grad[1], running_reward)
+  for k, v in model.iteritems():
+    g = grad_buffer[k] # gradient
+    rmsprop_cache[k] = decay_rate * rmsprop_cache[k] + (1 - decay_rate) * g ** 2
+    model[k] += learning_rate * g / (np.sqrt(rmsprop_cache[k]) + 1e-5)
+    grad_buffer[k] = np.zeros_like(v) # reset batch gradient buffer
+  batch_num += 1
+  if batch_num % 10 == 0: pickle.dump(model, open("save.p", "wb"))
diff --git a/examples/rl_pong/functions.py b/examples/rl_pong/functions.py
new file mode 100644
index 000000000..85cf4a858
--- /dev/null
+++ b/examples/rl_pong/functions.py
@@ -0,0 +1,85 @@
+# This code is copied and adapted from Andrej Karpathy's code for learning to
+# play Pong https://gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5.
+
+import ray
+import numpy as np
+import gym
+
+env = gym.make("Pong-v0")
+D = 80 * 80
+gamma = 0.99 # discount factor for reward
+def sigmoid(x):
+  return 1.0 / (1.0 + np.exp(-x)) # sigmoid "squashing" function to interval [0,1]
+
+def preprocess(I):
+  """preprocess 210x160x3 uint8 frame into 6400 (80x80) 1D float vector"""
+  I = I[35:195] # crop
+  I = I[::2,::2,0] # downsample by factor of 2
+  I[I == 144] = 0 # erase background (background type 1)
+  I[I == 109] = 0 # erase background (background type 2)
+  I[I != 0] = 1 # everything else (paddles, ball) just set to 1
+  return I.astype(np.float).ravel()
+
+def discount_rewards(r):
+  """take 1D float array of rewards and compute discounted reward"""
+  discounted_r = np.zeros_like(r)
+  running_add = 0
+  for t in reversed(xrange(0, r.size)):
+    if r[t] != 0: running_add = 0 # reset the sum, since this was a game boundary (pong specific!)
+    running_add = running_add * gamma + r[t]
+    discounted_r[t] = running_add
+  return discounted_r
+
+def policy_forward(x, model):
+  h = np.dot(model["W1"], x)
+  h[h < 0] = 0 # ReLU nonlinearity
+  logp = np.dot(model["W2"], h)
+  p = sigmoid(logp)
+  return p, h # return probability of taking action 2, and hidden state
+
+def policy_backward(eph, epx, epdlogp, model):
+  """backward pass. (eph is array of intermediate hidden states)"""
+  dW2 = np.dot(eph.T, epdlogp).ravel()
+  dh = np.outer(epdlogp, model["W2"])
+  dh[eph <= 0] = 0 # backpro prelu
+  dW1 = np.dot(dh.T, epx)
+  return {"W1": dW1, "W2": dW2}
+
+@ray.remote([dict], [tuple])
+def compgrad(model):
+  observation = env.reset()
+  prev_x = None # used in computing the difference frame
+  xs, hs, dlogps, drs = [], [], [], []
+  reward_sum = 0
+  done = False
+  while not done:
+    cur_x = preprocess(observation)
+    x = cur_x - prev_x if prev_x is not None else np.zeros(D)
+    prev_x = cur_x
+
+    aprob, h = policy_forward(x,model)
+    action = 2 if np.random.uniform() < aprob else 3 # roll the dice!
+
+    xs.append(x) # observation
+    hs.append(h) # hidden state
+    y = 1 if action == 2 else 0 # a "fake label"
+    dlogps.append(y - aprob) # grad that encourages the action that was taken to be taken (see http://cs231n.github.io/neural-networks-2/#losses if confused)
+
+    observation, reward, done, info = env.step(action)
+    reward_sum += reward
+
+    drs.append(reward) # record reward (has to be done after we call step() to get reward for previous action)
+
+  epx = np.vstack(xs)
+  eph = np.vstack(hs)
+  epdlogp = np.vstack(dlogps)
+  epr = np.vstack(drs)
+  xs, hs, dlogps, drs = [], [], [], [] # reset array memory
+
+  # compute the discounted reward backwards through time
+  discounted_epr = discount_rewards(epr)
+  # standardize the rewards to be unit normal (helps control the gradient estimator variance)
+  discounted_epr -= np.mean(discounted_epr)
+  discounted_epr /= np.std(discounted_epr)
+  epdlogp *= discounted_epr # modulate the gradient with advantage (PG magic happens right here.)
+  return (policy_backward(eph, epx, epdlogp, model), reward_sum)
diff --git a/examples/rl_pong/visualizer.py b/examples/rl_pong/visualizer.py
new file mode 100644
index 000000000..83eeef1b9
--- /dev/null
+++ b/examples/rl_pong/visualizer.py
@@ -0,0 +1,17 @@
+import matplotlib
+from matplotlib import pyplot as plt
+import pickle
+import numpy as np
+
+matplotlib.use("Agg")
+
+logs = pickle.load(open("logs_rl_original.p", "rb"))
+times_og = range(1, (len(logs) + 1))
+reward_og = map(lambda x:x[2], logs)
+plt.plot(times_og, reward_og)
+plt.savefig("original_batchnum_graph")
+logs = pickle.load(open("logs_rl_ray.p", "rb"))
+times_ray = range(1, (len(logs) + 1))
+reward_ray = map(lambda x: x[2], logs)
+plt.plot(times_ray, reward_ray)
+plt.savefig("rl_pong_graph")
diff --git a/examples/rl_pong/worker.py b/examples/rl_pong/worker.py
new file mode 100644
index 000000000..9c3b26510
--- /dev/null
+++ b/examples/rl_pong/worker.py
@@ -0,0 +1,17 @@
+import argparse
+import ray
+import ray.worker as worker
+import gym
+
+import functions
+
+parser = argparse.ArgumentParser(description="Parse addresses for the worker to connect to.")
+parser.add_argument("--scheduler-address", default="127.0.0.1:10001", type=str, help="the scheduler's address")
+parser.add_argument("--objstore-address", default="127.0.0.1:20001", type=str, help="the objstore's address")
+parser.add_argument("--worker-address", default="127.0.0.1:40001", type=str, help="the worker's address")
+
+if __name__ == '__main__':
+  args = parser.parse_args()
+  ray.connect(args.scheduler_address, args.objstore_address, args.worker_address)
+  ray.register_module(functions)
+  worker.main_loop()