Hands-On Reinforcement Learning for Games

Implementing a greedy policy

Our current value learner is not really learning aside from finding the optimum calculated value or the reward for each action over several episodes. Since our agent is not learning, it also makes it a less efficient learner as well. After all, the agent is just randomly picking any arm each episode when it could be using its acquired knowledge, which is the Value function, to determine it's next best choice. We can code this up in a very simple policy called a greedy policy in the next exercise:

1. Open up the Chapter_1_2.py example. The code is basically the same as our last example except for the episode iteration and, in particular, the selection of action or arm. The full listing can be seen here—note the new ...