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

Micheal Lanham

 
9781839214936: Hands-On Reinforcement Learning for Games: Implementing self-learning agents in games using artificial intelligence techniques
Couverture souple
ISBN 10 : 1839214937 ISBN 13 : 9781839214936
Editeur : Packt Publishing, 2020

Synopsis

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

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l?auteur

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Packt Publishing
Date d'édition
2020
Langue
anglais
ISBN 10 
1839214937
ISBN 13 
9781839214936
Reliure
Broché
Nombre de pages
432
Coordonnées du fabricant
non disponible
Personne responsable
gpsr@libri.de
gpsr@libri.de

Friedensallee 273
Hamburg
22763
Allemagne