def train(self):
self.build_model()
self.__model.summary()
self.__model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
UP_ACTION = 2
DOWN_ACTION = 3
# hyperparameters
gamma = .99
# initializing variables
x_train, y_train, rewards = [], [], []
reward_sum = 0
episode_nb = 0
# initialize variables
resume = True
running_reward = None
epochs_before_saving = 10
log_dir = './log' + datetime.now().strftime("%Y%m%d-%H%M%S") + "/"
# load pre-trained model if exist
if (resume and os.path.isfile(LOG_DIR + 'my_model_weights.h5')):
print("loading previous weights")
self.__model.load_weights(LOG_DIR + 'my_model_weights.h5')
# add a callback tensorboard object to visualize learning
tbCallBack = callbacks.TensorBoard(log_dir=log_dir, histogram_freq=0, \
write_graph=True, write_images=True)
# initializing environment
env = gym.make('Pong-v0')
observation = env.reset()
prev_input = None
# main loop
while (True):
# preprocess the observation, set input as difference between images
cur_input = prepro(observation)
x = cur_input - prev_input if prev_input is not None else np.zeros(
80 * 80)
prev_input = cur_input
# forward the policy network and sample action according to the proba distribution
proba = self.__model.predict(np.expand_dims(x, axis=1).T)
action = UP_ACTION if np.random.uniform() < proba else DOWN_ACTION
y = 1 if action == 2 else 0 # 0 and 1 are our labels
# log the input and label to train later
x_train.append(x)
y_train.append(y)
# do one step in our environment
observation, reward, done, info = env.step(action)
rewards.append(reward)
reward_sum += reward
# end of an episode
if done:
print('At the end of episode', episode_nb,
'the total reward was :', reward_sum)
# increment episode number
episode_nb += 1
# training
self.__model.fit(x=np.vstack(x_train), y=np.vstack(y_train), verbose=1, callbacks=[tbCallBack], \
sample_weight=discount_rewards(rewards, gamma))
# Saving the weights used by our model
if episode_nb % epochs_before_saving == 0:
self.__model.save_weights(
'my_model_weights' +
datetime.now().strftime("%Y%m%d-%H%M%S") + '.h5')
# Log the reward
running_reward = reward_sum if running_reward is None else running_reward * 0.99 + reward_sum * 0.01
tflog('running_reward', running_reward, custom_dir=log_dir)
# Reinitialization
x_train, y_train, rewards = [], [], []
observation = env.reset()
reward_sum = 0
prev_input = None
# load pre-trained model if exist
if (os.path.isfile('my_model_weights.h5')):
print("loading previous weights")
model.load_weights('my_model_weights.h5')
from karpathy import prepro, discount_rewards
import numpy as np
prev_input = None
# main loop
for i in range(STEPS):
# choose random action
# preprocess the observation, set input as difference between images
cur_input = prepro(observation)
x = cur_input - prev_input if prev_input is not None else np.zeros(80 * 80)
prev_input = cur_input
# forward the policy network and sample action according to the proba distribution
proba = model.predict(np.expand_dims(x, axis=1).T)
action = UP_ACTION if np.random.uniform() < proba else DOWN_ACTION
# run one step
observation, reward, done, info = env.step(action)
frames.append(observation) # collecting observation
# if episode is over, reset to beginning
if done:
observation = env.reset()
frames.append(observation) # collecting observation