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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
Concepts
Author (1):
Maxim Lapan Maxim Lapan
Profile icon Maxim Lapan
LinkedIn
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.
See other products by 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

Tensors

A tensor is the fundamental building block of all DL toolkits. The name sounds rather mystical, but the underlying idea is that a tensor is a multi-dimensional array. Using the analogy of school math, one single number is like a point, which is zero-dimensional, while a vector is one-dimensional like a line segment, and a matrix is a two-dimensional object. Three-dimensional number collections can be represented by a parallelepiped of numbers, but they don't have a separate name in the same way as a matrix. We can keep the term "tensor" for collections of higher dimensions.

Another thing to note about tensors used in DL is that they are only partially related to tensors used in tensor calculus or tensor algebra. In DL, a tensor is any multi-dimensional array, but in mathematics, a tensor is a mapping between vector spaces, which might be represented as a multi-dimensional array in some cases, but has much more semantical payload behind it. Mathematicians usually...

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Authors (1)

Profile icon Maxim Lapan
LinkedIn
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.
See other products by Maxim Lapan

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