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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

MountainCar experiments

In this section, we will try to implement and compare the effectiveness of different exploration approaches on a simple, but still challenging, environment, which could be classified as a “classical RL” problem that is very similar to the familiar CartPole problem. But in contrast to CartPole, the MountainCar problem is quite challenging from an exploration point of view.

The problem’s illustration is shown in Figure 18.3 and it consists of a small car starting from the bottom of the valley. The car can move left and right, and the goal is to reach the top of the mountain on the right.

PIC

Figure 18.3: The MountainCar environment

The trick here is in the environment’s dynamics and the action space. To reach the top, the actions need to be applied in a particular way to swing the car back and forth to speed it up. In other words, the agent needs to apply the actions for several...

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