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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?

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. 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

Environments

Most of the environments that include continuous action spaces are related to the physical world, so physics simulations are normally used. There are lots of software packages that can simulate physical processes, from very simple, open-source tools to complex, commercial packages that can simulate multiphysics processes (such as fluid, burning, and strength simulations). In the case of robotics, one of the most popular packages is MuJoCo, which stands for Multi-Joint Dynamics with Contact (www.mujoco.org). This is a physics engine in which you can define the components of the system, their interaction and properties. Then the simulator is responsible for solving the system by taking into account your intervention and finding the parameters (usually the location, velocities, and accelerations) of the components. This makes it ideal as a playground for RL environments, as you can define fairly complicated systems (such as multipede robots or robotic arms or humanoids) and ...

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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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