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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

Things to try

Here is a list of things you can do to improve your understanding of the topic:

In the D4PG code, I used a simple replay buffer, which was enough to get good an improvement over DDPG. You can try to switch the example to the prioritized replay buffer in the same way as we did in Chapter 7, DQN Extensions, and check the effect.
There are lots of interesting and challenging environments around. For example, you can start with other PyBullet environments, but there is also DeepMind Control Suite (there was a paper about it published at the beginning of 2018, comparing the A3C, DDPG, and D4PG methods), MuJoCo-based environments in Gym and lots of others.
You can request the trial license of MuJoCo and compare its stability, performance and resulting policy with PyBullet.
Play with the very challenging Learning how to run competition from NIPS-2017, where you are given a simulator of the human body and your agent needs to figure out how to move it around.

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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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