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Deep Reinforcement Learning Hands-On. - Second Edition

You're reading from  Deep Reinforcement Learning Hands-On. - Second Edition

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781838826994
Pages 826 pages
Edition 2nd Edition
Languages
Author (1):
Maxim Lapan Maxim Lapan
Profile icon Maxim Lapan

Table of Contents (28) Chapters

Preface 1. What Is Reinforcement Learning? 2. OpenAI Gym 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

Why exploration is important

In this book, lots of environments and methods have been discussed and in almost every chapter, exploration was mentioned. Very likely, you've already got ideas about why it's important to explore the environment effectively, so I'm just going to give a list of the main reasons.

Before that, it might be useful to agree on the term "effective exploration." In theoretical RL, a strict definition of this exists, but the high-level idea is simple and intuitive. Exploration is effective when we don't waste time in states of the environment that have already been seen by and are familiar to the agent. Rather than taking the same actions again and again, the agent needs to look for a new experience. As we've already discussed, exploration has to be balanced by exploitation, which is the opposite and means using our knowledge to get the best reward in the most efficient way. Let's now quickly discuss why we might be interested...

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