Hands-On Reinforcement Learning for Games: Implementing self-learning agents in games using artificial intelligence techniques
Authors: Micheal Lanham
ISBN-10: 1839214937
ISBN-13: 9781839214936
Released: 2020-01-03
Print Length 页数: 432 pages
Publisher finelybook 出版社: Packt
Book Description
Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch,OpenAI Gym,and TensorFlow
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.
What you will learn
Understand how deep learning can be integrated into an RL agent
Explore basic to advanced algorithms commonly used in game development
Build agents that can learn and solve problems in all types of environments
Train a Deep Q-Network (DQN) agent to solve the CartPole balancing problem
Develop game AI agents by understanding the mechanism behind complex AI
Integrate all the concepts learned into new projects or gaming agents
Contents
Section 1: Exploring the Environment
Chapter 01: Understanding Rewards-Based Learning
Chapter 02: Dynamic Programming and the Bellman Equation
Chapter 03: Monte Carlo Methods
Chapter 04: Temporal Difference Learning
Chapter 05: Exploring SARSA
Section 2: Exploiting the Knowledge
Chapter 06: Going Deep with DQN
Chapter 07: Going Deeper with DDQN
Chapter 08: Policy Gradient Methods
Chapter 09: Optimizing for Continuous Control
Chapter 10: All about Rainbow DQN
Chapter 11: Exploiting ML-Agents
Chapter 12: DRL Frameworks
Section 3: Reward Yourself
3D Worlds
Chapter 13: From DRL to AGI
Chapter 14: Other Books You May Enjoy
Index
