""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym
# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward
decay_rate = 0.99 # decay factor for RMSProp leaky sum of grad^2
resume = False # resume from previous checkpoint?
render = False
# model initialization
D = 80 * 80 # 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
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-x)) # sigmoid "squashing" function to interval [0,1]
def prepro(I):
"""prepro 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):
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, epdlogp):
"""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}
env = gym.make("Pong-v0")
observation = env.reset()
prev_x = None # used in computing the difference frame
xs, hs, dlogps, drs = [], [], [], []
running_reward = None
reward_sum = 0
episode_number = 0
while True:
if render:
env.render()
# preprocess the observation, set input to network to be difference image
cur_x = prepro(observation)
x = cur_x - prev_x if prev_x is not None else np.zeros(D)
prev_x = cur_x
# forward the policy network and sample an action from the returned probability
aprob, h = policy_forward(x)
action = 2 if np.random.uniform() < aprob else 3 # roll the dice!
# record various intermediates (needed later for backprop)
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)
# step the environment and get new measurements
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)
if done: # an episode finished
episode_number += 1
# stack together all inputs, hidden states, action gradients, and rewards for this episode
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.)
grad = policy_backward(eph, epdlogp)
for k in model:
grad_buffer[k] += grad[k] # accumulate grad over batch
# perform rmsprop parameter update every batch_size episodes
if episode_number % batch_size == 0:
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
# boring book-keeping
running_reward = reward_sum if running_reward is None else running_reward * 0.99 + reward_sum * 0.01
print("resetting env. episode reward total was %f. running mean: %f" % (reward_sum, running_reward))
if episode_number % 100 == 0:
pickle.dump(model, open("save.p", "wb"))
reward_sum = 0
observation = env.reset() # reset env
prev_x = None
if reward != 0: # Pong has either +1 or -1 reward exactly when game ends.
print("ep %d: game finished, reward: %f" % (episode_number, reward)) + ("" if reward == -1 else " !!!!!!!!")