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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

GA on Cheetah

In our final example in this chapter, we'll implement the parallelized deep GA on the HalfCheetah environment. The complete code is in Chapter16/04_cheetah_ga.py. The architecture is very close to the parallel ES version, with one master process and several workers. The goal of every worker is to evaluate the batch of networks and return the result to the master, which merges partial results into the complete population, ranks the individuals according to the obtained reward and generates the next population to be evaluated by the workers.

Every individual is encoded by a list of random seeds used to initialize the initial network weights and all subsequent mutations. This representation allows very compact encoding of the network, even when the number of parameters in the policy is not very large. For example, in our network with two hidden layers of 64 neurons, we have 6278 float values (the input is 26 values and the action is six floats). Every float occupies 4 bytes...

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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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