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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

PyTorch Ignite

PyTorch is an elegant and flexible library, which makes it a favorite choice for thousands of researchers, DL enthusiasts, industry developers, and others. But flexibility has its own price: too much code to be written to solve your problem. Sometimes, this is very beneficial, such as when implementing some new optimization method or DL trick that hasn't been included in the standard library yet. Then you just implement the formulas using Python and PyTorch magic will do all the gradients and backpropagation machinery for you. Another example is in situations when you have to work on a very low level, fiddling with gradients, optimizer details, and the way your data is transformed by the NN.

However, sometimes you don't need this flexibility, which happens when you work on routine tasks, like the simple supervised training of an image classifier. For such tasks, standard PyTorch might be at too low a level when you need to deal with the same code over and...

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