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Key benefits

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

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.

Who is this book 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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Hands-On Reinforcement Learning for Games

S$59.99

Deep Learning with TensorFlow 2 and Keras

S$59.99

S$66.99

Total S$ 186.97

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Elham Jahandide Feb 19, 2021

I read this book cover-to-cover. I believe this is among the best books in the RL area. Very easy to read, each section starts with explaining the basic concepts without too many math complications. What makes this book very unique is the implementation of every single algorithm in python. Having this, the readers can observe the step-by-step implementations as well as the numerical performance of the algorithms. Further, this book is very comprehensive and you can find a broad range of classical and new algorithms. I highly recommend this book to everyone who wants to start learning RL and use it in the carrier.

Amazon Verified review

Amazon Customer Sep 25, 2020

Basically, this book would be a great choice if you want to get familiar with popular algorithms in reinforcement learning, play with them, test your skills, and apply them. This book covers several popular test environments for RL, the basic but also important algorithms, and most importantly, every detail of the code. After reading it, you should be very familiar with all aspects of implementing those RL algorithms.However, if you are not only interested in using them, but also the theory behind those algorithms, you should read the original papers. This book does cover something about RL theory but would not be enough.And one small suggestion, the tips now are taking too much space in the pages, perhaps making them smaller would be a good idea?

MrSorvisto Oct 20, 2020

I've read a few books and papers on reinforcement learning and they often fall short but this book was exciting to read as each chapter builds on foundations from previous chapters with hand-on demos that walk you through a real world application. Gaming is also a great strategy this book uses to take the reader through the intricate details of RL building up to SARSA and policy gradient methods and deep Q-learning in a linear way. Necessary theory on Markov decision processes, Monte Carlo methods and dynamic programming techniques are all covered in the right amount of detail and the reader is rewarded along the way with many hands-on walk throughs you can implement yourself in Python through Keras-RL, PyTorch and Unity to build intelligent policy-based agents in 3D worlds. The book also hints at the philosophical nature of AI and a path forward from RL to AGI. Highly recommended if you're looking to deepen your knowledge of deep reinforcement learning or reward yourself with hands-on demos on reinforcement learning with a fun, intuitive bottom up approach to learning

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

What do you get with eBook?