1. How to add Atari module
*From Terminal:
cd gym;
brew install cmake boost boost-python sdl2 swig wget;
pip install -e '.[atari]’
*From PyCharm install:
atari-py
2. Source code
Reference: https://gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5)
Below is bit modified version to fix certain errors due to versions of libraries,
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import _pickle as pickle
#import cPickle as pickle
import gym
# hyperparameters
H = 200 # number of hidden layer neuronsbatch_size = 10 # every how many episodes to do a param update?#learning_rate = 1e-4
learning_rate = 1e-2gamma = 0.99 # discount factor for rewarddecay_rate = 0.99 # decay factor for RMSProp leaky sum of grad^2resume = False # resume from previous checkpoint?render = False
# model initialization
D = 80 * 80 # input dimensionality: 80x80 gridif 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.items()} # update buffers that add up gradients over a batchrmsprop_cache = {k: np.zeros_like(v) for k, v in model.items()} # 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(range(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 framexs, hs, dlogps, drs = [], [], [], []
running_reward = Nonereward_sum = 0episode_number = 0while 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.items():
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))
