Created
July 19, 2021 20:34
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Minor update to Andrej Karpathy's Deep Reinforcement Learning blog post (https://karpathy.github.io/2016/05/31/rl/) to support Python 3.
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import numpy as np | |
import pickle | |
import gym | |
# hyperparameters | |
H = 200 # number of hidden layer neurons | |
batch_size = 10 # after how many episodes do we do a parameter update? | |
learning_rate = 3e-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 prior 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.items() } # update buffers that add up gradients over a batch | |
rmsprop_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 a 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.float64).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 (specific to Pong!) | |
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, as well as hidden state | |
def policy_backward (eph, epdlogp): | |
""" backward pass. (eph is an 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 intermediaries (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 | |
# 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 the reward for the 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 | |
# book-keeping work | |
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 environment | |
prev_x = None | |
if reward != 0: # Pong has either +1 or -1 reward exactly when the game ends. | |
print('ep %d: game finished, reward: %f' % (episode_number, reward) + ('' if reward == -1 else ' !!!!!!' )) | |
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