Advanced Deep Learning with Keras

Chapter 9
# discount factor
self.gamma = 0.9
# initially 90% exploration, 10% exploitation
self.epsilon = 0.9
# iteratively applying decay til 10% exploration/90%
exploitation
self.epsilon_decay = decay
self.epsilon_min = 0.1
# learning rate of Q-Learning
self.learning_rate = 0.1
# file where Q Table is saved on/restored fr
if slippery:
self.filename = 'q-frozenlake-slippery.npy'
else:
self.filename = 'q-frozenlake.npy'
# demo or train mode
self.demo = demo
# if demo mode, no exploration
if demo:
self.epsilon = 0
# determine the next action
# if random, choose from random action space
# else use the Q Table
def act(self, state, is_explore=False):
# 0 - left, 1 - Down, 2 - Right, 3 - Up
if is_explore or np.random.rand() < self.epsilon:
# explore - do random action
return self.action_space.sample()
# exploit - choose action with max Q-value
return np.argmax(self.q_table[state])
# TD(0) learning (generalized Q-Learning) with learning rate
def update_q_table(self, state, action, reward, next_state):
# Q(s, a) += alpha * (reward + gamma * max_a' Q(s', a') - Q
(s, a))
q_value = self.gamma * np.amax(self.q_table[next_state])
q_value += reward
q_value -= self.q_table[state, action]
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