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Deep Reinforcement Learning Hands-On

You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781788834247

Length 546 pages

Edition 1st Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Maxim Lapan

View More author details

Table of Contents (21) Chapters

Preface

1. What is Reinforcement Learning? FREE CHAPTER

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. DQN Extensions

8. Stocks Trading Using RL

9. Policy Gradients – An Alternative

10. The Actor-Critic Method

11. Asynchronous Advantage Actor-Critic

12. Chatbots Training with RL

13. Web Navigation

14. Continuous Action Space

15. Trust Regions – TRPO, PPO, and ACKTR

16. Black-Box Optimization in RL

17. Beyond Model-Free – Imagination

18. AlphaGo Zero

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Index

Custom layers

In the previous section, we briefly mentioned the nn.Module class as a base parent for all NN building blocks exposed by PyTorch. It's not only a unifying parent for the existing layers—it's much more than that. By subclassing the nn.Module class, you can create your own building blocks which can be stacked together, reused later, and integrated into the PyTorch framework flawlessly.

At its core, nn.Module provides quite rich functionality to its children:

It tracks all submodules that the current module includes. For example, your building block can have two feed-forward layers used somehow to perform the block's transformation.
It provides functions to deal with all parameters of the registered submodules. You can obtain a full list of the module's parameters (parameters() method), zero its gradients (zero_grads() method), move to CPU or GPU (to(device) method), serialize and deserialize the module (state_dict() and load_state_dict()), and even...

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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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