Book Image

Hands-On Reinforcement Learning for Games

By : Micheal Lanham
Book Image

Hands-On Reinforcement Learning for Games

By: Micheal Lanham

Overview of this book

With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python. Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games. By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications.
Table of Contents (19 chapters)
1
Section 1: Exploring the Environment
7
Section 2: Exploiting the Knowledge
15
Section 3: Reward Yourself

Understanding the TCA problem

The credit assignment problem is described as the task of understanding what actions you need to take to receive the most credit or, in the case of RL, rewards. RL solves the credit assignment problem by allowing an algorithm or agent to find the optimum set of actions to maximize the rewards. In all of our previous chapters, we have seen how variations of this can be done with DP and MC methods. However, both of these previous methods are offline, so they cannot learn while performing a task.

The TCA problem is differentiated from the credit assignment CA problem in that it needs to be solved across time; that is, an algorithm needs to find the best policy across time steps instead of learning after an episode, in the case of MC, or needing to plan before, as DP does. This also means that an algorithm that solves the CA problem across time can also...