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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

Value, state, and optimality

You may remember our definition of the value of the state in Chapter 1, What is Reinforcement Learning?. This is a very important notion and the time has come to explore it further. This whole part of the book is built around the value and how to approximate it. We defined value as an expected total reward that is obtainable from the state. In a formal way, the value of the state is: Value, state, and optimality , where is the local reward obtained at the step t of the episode.

The total reward could be discounted or not; it's up to us how to define it. Value is always calculated in the respect of some policy that our agent follows. To illustrate, let's consider a very simple environment with three states:

The agent's initial state.
The final state that the agent is in after executing action "left" from the initial state. The reward obtained from this is 1.
The final state that the agent is in after action "down". The reward obtained from this is 2...

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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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