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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

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781839214936
Length 432 pages
Edition 1st Edition
Languages
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Author (1):
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Micheal Lanham Micheal Lanham
Author Profile Icon Micheal Lanham
Micheal Lanham
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Table of Contents (19) Chapters Close

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

Going Deep with DQN

In this chapter, you will be introduced to deep learning (DL) in order to handle newer, more challenging infinite Markov decision process (MDP) problems. We will cover some basics about DL that are relevant to reinforcement learning (RL), and then look at how we can solve a Q-learning. After that, we will look at how to build a Deep Q-learning or DQN agent in order to solve some Gym environments.

Here is a summary of the topics we will cover in this chapter:

  • DL for RL
  • Using PyTorch for DL
  • Building neural networks with PyTorch
  • Understanding DQN in PyTorch
  • Exercising DQN

In this chapter, we introduce DL with respect to RL. Applying DL to deep reinforcement learning (DRL) is quite specific and is not covered in detail here.

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