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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

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838826994

Length 826 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Chatbots

Author (1):

Maxim Lapan

View More author details

Table of Contents (28) Chapters

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

Index

Symbols

2×2 cube model 763, 764, 765

3×3 cube model

A

A2C

agent, adding 333, 334, 335

using, on Pong 318, 319, 320, 321, 322, 323, 324

using, on Pong results 324, 325, 326, 327

with data parallelism 334

with gradients parallelism 334

A2C method

about 505

implementation 506, 508, 510

models, used for video recording 512

results 510, 511, 512

A3C, with data parallelism

about 336

implementation 336, 338, 339, 340, 341, 342, 343, 344

result 344

A3C, with with gradients parallelism

about 346, 347

implementation 347, 348, 349, 350, 351, 352

results 352

ACKTR

about 616

implementation 617

results 617, 618

actions 10

action selectors 166, 167

action selectors, cases

argmax 166

policy-based 166

action space 22

actor-critic method 638

about 316, 317

advantage 316

considerations 317, 318

Adam algorithm 322

advantage actor-critic (A2C...

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Authors (1)

Maxim Lapan

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.

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