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

Hands-On Reinforcement Learning for Games: Implementing self-learning agents in games using artificial intelligence techniques
Hands-On Reinforcement Learning for Games: Implementing self-learning agents in games using artificial intelligence techniques by Micheal Lanham
English | 2020 | ISBN: 1839214936 | 432 Pages | True PDF, EPUB | 85 MB

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