Advanced Deep Learning with Keras

Deep Reinforcement Learning
self.col = 4
# 6 states
self.row = 6
# setup the environment
self.q_table = np.zeros([self.row, self.col])
self.init_transition_table()
self.init_reward_table()
# discount factor
self.gamma = 0.9
# 90% exploration, 10% exploitation
self.epsilon = 0.9
# exploration decays by this factor every episode
self.epsilon_decay = 0.9
# in the long run, 10% exploration, 90% exploitation
self.epsilon_min = 0.1
# reset the environment
self.reset()
self.is_explore = True
# start of episode
def reset(self):
self.state = 0
return self.state
# agent wins when the goal is reached
def is_in_win_state(self):
return self.state == 2
def init_reward_table(self):
"""
0 - Left, 1 - Down, 2 - Right, 3 - Up
----------------
| 0 | 0 | 100 |
----------------
| 0 | 0 | -100 |
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