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])
})[0]
y, x = np.unravel_index(a, (field_size, field_size))
s1, r, d = game.action(y, x, False)
total_steps += 1
episodeBuffer.add(
np.reshape(np.array([s, a, r, s1, d]),
[1, 5])) # Save experience to episode buffer.
if total_steps > pre_train_steps and total_steps % update_freq:
trainBatch = myBuffer.sample(
batch_size) # Get a random batch of experiences.
# Below we perform the Double-DQN update to the target Q-values
Q1 = sess.run(mainQN.predict,
feed_dict={
mainQN.input:
np.reshape(np.stack(trainBatch[:, 3]),
[-1, field_size, field_size, 2])
})
