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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

OpenAI Universe

OpenAI Universe is available in OpenAI's GitHub repository https://github.com/openai/universe and its core idea is to wrap general GUI applications into an RL environment using the same core classes provided by Gym. To achieve this, it uses the VNC protocol to connect with the VNC server running inside the docker container, exposing the mouse and keyboard actions to the RL agent and providing the GUI application image as an observation. The reward is provided by an external small "rewarder" daemon running inside the same container and giving the agent scalar reward value based on this rewarder judgement. It is possible to launch several containers locally, or over the network, to gather episodes data in parallel, in the same way that we started several Atari emulators to increase the convergence of the Actor-Critic (A2C) method in Chapter 11, Asynchronous Advantage Actor‑Critic. The architecture is illustrated in the following diagram:

OpenAI Universe

Figure 2: OpenAI...

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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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