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Deep Reinforcement Learning Hands-On

You're reading from   Deep Reinforcement Learning Hands-On A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781835882702
Length 716 pages
Edition 3rd Edition
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Author (1):
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Maxim Lapan Maxim Lapan
Author Profile Icon Maxim Lapan
Maxim Lapan
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Toc

Table of Contents (29) Chapters Close

Preface 1. Part 1 Introduction to RL FREE CHAPTER
2. What Is Reinforcement Learning? 3. OpenAI Gym API and Gymnasium 4. Deep Learning with PyTorch 5. The Cross-Entropy Method 6. Part 2 Value-based methods
7. Tabular Learning and the Bellman Equation 8. Deep Q-Networks 9. Higher-Level RL Libraries 10. DQN Extensions 11. Ways to Speed Up RL 12. Stocks Trading Using RL 13. Part 3 Policy-based methods
14. Policy Gradients 15. Actor-Critic Method: A2C and A3C 16. The TextWorld Environment 17. Web Navigation 18. Part 4 Advanced RL
19. Continous Action Space 20. Trust Region Methods 21. Black-Box Optimizations in RL 22. Advanced Exploration 23. Reinforcement Learning with Human Feedback 24. AlphaGo Zero and MuZero 25. RL in Discrete Optimization 26. Multi-Agent RL 27. Bibliography
28. Index

Getting started with the environment

Before we jump into our first MARL example, let’s look at the environment we can use. If you want to play with MARL, your choice is a bit limited. All the environments that come with Gym support only one agent. There are some patches for Atari Pong, to switch it into two-player mode, but they are not standard and are an exception rather than the rule.

DeepMind, together with Blizzard, has made StarCraft II publicly available ( https://github.com/deepmind/pysc2) and makes for a very interesting and challenging environment for experimentation. However, for somebody who is taking their first steps in MARL, it might be too complex. In that regard, I have found the MAgent environment, originally developed by Geek.AI, to be perfectly suitable; it is simple and fast and has minimal dependency, but it still allows you to simulate different multi-agent scenarios for experimentation. It doesn’t provide a Gym-compatible API, but we...

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