Hands-On Reinforcement Learning for Games

Implementing a Q-learning agent

While that Q-learning equation may seem a lot more complex, actually implementing the equation is not unlike building our agent that just learned values earlier. To keep things simpler, we will use the same base of code but turn it into a Q-learning example. Open up the code example, Chapter_1_4.py, and follow the exercise here:

1. Here is the full code listing for reference:

import randomarms = 7bandits = 7learning_rate = .1gamma = .9episodes = 10000reward = []for i in range(bandits):      reward.append([])           for j in range(arms):         reward[i].append(random.uniform(-1,1))print(reward)Q = []for i in range(bandits):      Q.append([])           for j in range(arms):         Q[i].append(10.0)print(Q)def greedy(values): return values.index(max(values)) ...