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

Imagination and reasoning in RL

Something that we can observe from our own experience of learning is how imagination can benefit the learning process. Pure imagination is the stuff of deep abstract thoughts and dreams, often closer to a hallucination than any way to solve a real problem. Except, this same imagination can be used to span gaps in our understanding of knowledge and allow us to reason out possible solutions. Say that we are trying to solve the problem of putting a puzzle together, and all we have are three remaining, mostly black pieces, as shown in the following image:

Imagining what the three missing puzzle pieces may look like

Given the simplicity of the preceding diagram, it is quite easy for us to imagine what those puzzle pieces may look like. We are able to fill in those gaps quite easily using our imagination from previous observations and reasoning. This...