Hands-On Reinforcement Learning for Games: Implementing self-learning agents in games using artificial intelligence techniques

Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow

Key Features

• Get to grips with the different reinforcement and DRL algorithms for game development
• Learn how to implement components such as artificial agents, map and level generation, and audio generation
• Gain insights into cutting-edge RL research and understand how it is similar to artificial general research

Book Description

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

Who this book is for

If you're a game developer looking to implement AI techniques to build next-generation games from scratch, this book is for you. Machine learning and deep learning practitioners, and RL researchers who want to understand how to use self-learning agents in the game domain will also find this book useful. Knowledge of game development and Python programming experience are required.

Table of Contents

1. Understanding Rewards-Based Learning
2. Dynamic Programming and the Bellman Equation
3. Monte Carlo Methods
4. Temporal Difference Learning
5. Exploring SARSA
6. Going Deep with DQN
7. Going Deeper with DDQN
8. Policy Gradient Methods
9. Optimizing for Continuous Control
10. All about Rainbow DQN
11. Exploiting ML-Agents
12. DRL Frameworks
13. 3D Worlds
14. From DRL to AGI

Micheal Lanham is a proven software and tech innovator with 20 years of experience. During that time, he has developed a broad range of software applications in areas such as games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries as an R&D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He was later introduced to Unity and has been an avid developer, consultant, manager, and author of multiple Unity games, graphic projects, and books ever since.