Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Deep Reinforcement Learning Hands-On

You're reading from   Deep Reinforcement Learning Hands-On Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Arrow left icon
Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781838826994
Length 826 pages
Edition 2nd Edition
Languages
Concepts
Arrow right icon
Author (1):
Arrow left icon
Maxim Lapan Maxim Lapan
Author Profile Icon Maxim Lapan
Maxim Lapan
Arrow right icon
View More author details
Toc

Table of Contents (28) Chapters Close

Preface 1. What Is Reinforcement Learning? 2. OpenAI Gym FREE CHAPTER 3. Deep Learning with PyTorch 4. The Cross-Entropy Method 5. Tabular Learning and the Bellman Equation 6. Deep Q-Networks 7. Higher-Level RL Libraries 8. DQN Extensions 9. Ways to Speed up RL 10. Stocks Trading Using RL 11. Policy Gradients – an Alternative 12. The Actor-Critic Method 13. Asynchronous Advantage Actor-Critic 14. Training Chatbots with RL 15. The TextWorld Environment 16. Web Navigation 17. Continuous Action Space 18. RL in Robotics 19. Trust Regions – PPO, TRPO, ACKTR, and SAC 20. Black-Box Optimization in RL 21. Advanced Exploration 22. Beyond Model-Free – Imagination 23. AlphaGo Zero 24. RL in Discrete Optimization 25. Multi-agent RL 26. Other Books You May Enjoy
27. 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. But in contrast to CartPole, the MountainCar problem is quite challenging from an exploration point of view.

The problem's illustration is shown in the following figure 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.

Figure 21.3: 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 time steps to make the car go faster and eventually...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime