Advanced Deep Learning with Keras

Chapter 9
# UI to dump Q Table contents
def print_q_table(self):
print("Q-Table (Epsilon: %0.2f)" % self.epsilon)
print(self.q_table)
# update Exploration-Exploitation mix
def update_epsilon(self):
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
Listing 9.3.2, q-learning-9.3.1.py. The main Q-Learning loop. The agent's Q-Table
is updated every state, action, reward, and next state iteration:
# state, action, reward, next state iteration
for episode in range(episode_count):
state = q_world.reset()
done = False
print_episode(episode, delay=delay)
while not done:
action = q_world.act()
next_state, reward, done = q_world.step(action)
q_world.update_q_table(state, action, reward, next_state)
print_status(q_world, done, step, delay=delay)
state = next_state
# if episode is done, perform housekeeping
if done:
if q_world.is_in_win_state():
wins += 1
scores.append(step)
if wins > maxwins:
print(scores)
exit(0)
# Exploration-Exploitation is updated every episode
q_world.update_epsilon()
step = 1
else:
step += 1
print(scores)
q_world.print_q_table()
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