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Hands-On Reinforcement Learning for Games
Hands-On Reinforcement Learning for Games

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

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Profile Icon Micheal Lanham
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S$42.99 S$47.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (3 Ratings)
eBook Jan 2020 432 pages 1st Edition
eBook
S$42.99 S$47.99
Paperback
S$59.99
Subscription
Free Trial
Arrow left icon
Profile Icon Micheal Lanham
Arrow right icon
S$42.99 S$47.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (3 Ratings)
eBook Jan 2020 432 pages 1st Edition
eBook
S$42.99 S$47.99
Paperback
S$59.99
Subscription
Free Trial
eBook
S$42.99 S$47.99
Paperback
S$59.99
Subscription
Free Trial

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Hands-On Reinforcement Learning for Games

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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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 03, 2020
Length: 432 pages
Edition : 1st
Language : English
ISBN-13 : 9781839216770
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Product Details

Publication date : Jan 03, 2020
Length: 432 pages
Edition : 1st
Language : English
ISBN-13 : 9781839216770
Vendor :
Google
Languages :
Tools :

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Table of Contents

18 Chapters
Section 1: Exploring the Environment Chevron down icon Chevron up icon
Understanding Rewards-Based Learning Chevron down icon Chevron up icon
Dynamic Programming and the Bellman Equation Chevron down icon Chevron up icon
Monte Carlo Methods Chevron down icon Chevron up icon
Temporal Difference Learning Chevron down icon Chevron up icon
Exploring SARSA Chevron down icon Chevron up icon
Section 2: Exploiting the Knowledge Chevron down icon Chevron up icon
Going Deep with DQN Chevron down icon Chevron up icon
Going Deeper with DDQN Chevron down icon Chevron up icon
Policy Gradient Methods Chevron down icon Chevron up icon
Optimizing for Continuous Control Chevron down icon Chevron up icon
All about Rainbow DQN Chevron down icon Chevron up icon
Exploiting ML-Agents Chevron down icon Chevron up icon
DRL Frameworks Chevron down icon Chevron up icon
Section 3: Reward Yourself Chevron down icon Chevron up icon
3D Worlds Chevron down icon Chevron up icon
From DRL to AGI Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(3 Ratings)
5 star 100%
4 star 0%
3 star 0%
2 star 0%
1 star 0%
Elham Jahandide Feb 19, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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
Amazon Customer Sep 25, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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?
Amazon Verified review Amazon
MrSorvisto Oct 20, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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
Amazon Verified review Amazon
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