Advanced Deep Learning with Keras

Deep Reinforcement Learning
q_value *= self.learning_rate
q_value += self.q_table[state, action]
self.q_table[state, action] = q_value
# dump Q Table
def print_q_table(self):
print(self.q_table)
print("Epsilon : ", self.epsilon)
# save trained Q Table
def save_q_table(self):
np.save(self.filename, self.q_table)
# load trained Q Table
def load_q_table(self):
self.q_table = np.load(self.filename)
# adjust epsilon
def update_epsilon(self):
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
Listing 9.5.2, q-frozenlake-9.5.1.py. The main Q-Learning loop for the
FrozenLake-v0 environment:
# loop for the specified number of episode
for episode in range(episodes):
state = env.reset()
done = False
while not done:
# determine the agent's action given state
action = agent.act(state, is_explore=args.explore)
# get observable data
next_state, reward, done, _ = env.step(action)
# clear the screen before rendering the environment
os.system('clear')
# render the environment for human debugging
env.render()
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