# Make a path for our model to be saved in.
if not os.path.exists(path):
os.makedirs(path)
print("Start training.")
with tf.Session() as sess:
sess.run(init)
if load_model:
print('Loading Model...')
ckpt = tf.train.get_checkpoint_state(path)
saver.restore(sess, ckpt.model_checkpoint_path)
for i in range(num_episodes):
episodeBuffer = qnetwork.ExperienceBuffer()
# Reset environment and get first new observation
game = Game(field_size, field_size, mines)
s = game.state()
d = False
rAll = 0
j = 0
actions = []
# The Q-Network
while j < max_epLength: # If the network takes more moves than needed for the field, cancel episode
j += 1
# Choose an action by greedily (with e chance of random action) from the Q-network
if np.random.rand(1) < e or total_steps < pre_train_steps:
a = np.random.randint(0, num_actions)
else:
a = sess.run(mainQN.predict,
feed_dict={
mainQN.input:
np.reshape(s, [-1, field_size, field_size, 2])
