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Hands-On Reinforcement Learning for Games

You're reading from Hands-On Reinforcement Learning for Games Implementing self-learning agents in games using artificial intelligence techniques

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781839214936

Length 432 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Author (1):

Micheal Lanham

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: Exploring the Environment

2. Understanding Rewards-Based Learning FREE CHAPTER

3. Dynamic Programming and the Bellman Equation

4. Monte Carlo Methods

5. Temporal Difference Learning

6. Exploring SARSA

7. Section 2: Exploiting the Knowledge

8. Going Deep with DQN

9. Going Deeper with DDQN

10. Policy Gradient Methods

11. Optimizing for Continuous Control

12. All about Rainbow DQN

13. Exploiting ML-Agents

14. DRL Frameworks

15. Section 3: Reward Yourself

16. 3D Worlds

17. From DRL to AGI

18. Other Books You May Enjoy

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Understanding the Bellman equation

Bellman worked on solving finite MDP with DP, and it was during these efforts he derived his famed equation. The beauty behind this equation—and more abstractly, the concept, in general—is that it describes a method of optimizing the value or quality of a state. In other words, it describes how we can determine the optimal value/quality for being in a given state given the action and choices of successive states. Before breaking down the equation itself, let's first reconsider the finite MDP in the next section.

Unraveling the finite MDP

Consider the finite MDP we developed in Chapter 1, Understanding Rewards Learning, that described your morning routine. Don't to...

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Authors (1)

Micheal Lanham

Micheal Lanham

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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