Advanced Deep Learning with Keras

Chapter 9
updates
# correction on the Q value for the action used
q_values[0][action] = reward if done else q_value
# collect batch state-q_value mapping
state_batch.append(state[0])
q_values_batch.append(q_values[0])
# train the Q-network
self.q_model.fit(np.array(state_batch),
np.array(q_values_batch),
batch_size=batch_size,
epochs=1,
verbose=0)
# update exploration-exploitation probability
self.update_epsilon()
# copy new params on old target after every 10 training
if self.replay_counter % 10 == 0:
self.update_weights()
self.replay_counter += 1
# decrease the exploration, increase exploitation
def update_epsilon(self):
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
Listing 9.6.2, dqn-cartpole-9.6.1.py. Training loop of DQN implementation
in Keras:
# Q-Learning sampling and fitting
for episode in range(episode_count):
state = env.reset()
state = np.reshape(state, [1, state_size])
done = False
total_reward = 0
while not done:
# in CartPole-v0, action=0 is left and action=1 is right
action = agent.act(state)
next_state, reward, done, _ = env.step(action)
# in CartPole-v0:
# state = [pos, vel, theta, angular speed]
next_state = np.reshape(next_state, [1, state_size])
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