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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

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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

Roboschool

To experiment with the methods in this chapter, we'll use roboschool, which uses PyBullet as a physics engine and has 13 environments of various complexity. PyBullet has similar environments, but at the time of writing it wasn't possible to create several instances of the same environment due to internal OpenGL issue. In this chapter, we'll get in touch with two problems: RoboschoolHalfCheetah-v1, which models a two-legged creature and RoboschoolAnt-v1, which has four legs. The state and action spaces of them are very similar to the Minitaur environment that we saw in the previous chapter: the state includes characteristics from joints and actions are activations of those joints. The goal for both is to move as far as possible, minimizing the energy spent.

Roboschool

Figure 1: Screenshots of two roboschool environments: RoboschoolHalfCheetah and RoboschoolAnt

To install roboschool, you need to follow the instructions on https://github.com/openai/roboschool. This requires...

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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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