Hands-On Reinforcement Learning for Games

Summary

In this chapter, we first introduced ourselves to the world of RL. We looked at what makes RL so unique and why it makes sense for games. After that, we explored the basic terminology and history of modern RL. From there, we looked to the foundations of RL and the Markov decision process, where we discovered what makes an RL problem. Then we looked to building our first learner a value learner that calculated the values of states on an action. This led us to uncover the need for exploration and exploitation and the dilemma that constantly challenges RL implementers. Next, we jumped in and discovered the full Q-learning equation and how to build a Q-learner, where we later realized that the full Q equation was beyond what we needed ...