Hands-On Reinforcement Learning for Games: Implementing self-learning agents in games using artificial intelligence techniques
Author:   Micheal Lanham
Publisher:   Packt Publishing Limited
ISBN:  

9781839214936


Pages:   432
Publication Date:   03 January 2020
Format:   Paperback
Availability:   Available To Order   Availability explained
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Hands-On Reinforcement Learning for Games: Implementing self-learning agents in games using artificial intelligence techniques


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Author:   Micheal Lanham
Publisher:   Packt Publishing Limited
Imprint:   Packt Publishing Limited
ISBN:  

9781839214936


ISBN 10:   1839214937
Pages:   432
Publication Date:   03 January 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Available To Order   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

Table of Contents

Table of Contents Understanding Rewards-Based Learning Dynamic Programming and the Bellman Equation Monte Carlo Methods Temporal Difference Learning Exploring SARSA Going Deep with DQN Going Deeper with DDQN Policy Gradient Methods Optimizing for Continuous Control All about Rainbow DQN Exploiting ML-Agents DRL Frameworks 3D Worlds From DRL to AGI

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Author Information

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.

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