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Deep Reinforcement Learning Hands-On. - Second Edition

You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Product type Book

Published in Jan 2020

Publisher Packt

ISBN-13 9781838826994

Pages 826 pages

Edition 2nd Edition

Languages

Concepts

Deep Reinforcement Learning

Author (1):

Maxim Lapan

Table of Contents (28) Chapters

Preface

What Is Reinforcement Learning?

OpenAI Gym

Deep Learning with PyTorch

The Cross-Entropy Method

Tabular Learning and the Bellman Equation

Deep Q-Networks

Higher-Level RL Libraries

DQN Extensions

Ways to Speed up RL

Stocks Trading Using RL

Policy Gradients – an Alternative

The Actor-Critic Method

Asynchronous Advantage Actor-Critic

Training Chatbots with RL

The TextWorld Environment

Web Navigation

Continuous Action Space

RL in Robotics

Trust Regions – PPO, TRPO, ACKTR, and SAC

Black-Box Optimization in RL

Advanced Exploration

Beyond Model-Free – Imagination

AlphaGo Zero

RL in Discrete Optimization

Multi-agent RL

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Index

The theoretical foundations of RL

In this section, I will introduce you to the mathematical representation and notation of the formalisms (reward, agent, actions, observations, and environment) that we just discussed. Then, using this as a knowledge base, we will explore the second-order notions of the RL language, including state, episode, history, value, and gain, which will be used repeatedly to describe different methods later in the book.

Markov decision processes

Before that, we will cover Markov decision processes (MDPs), which will be described like a Russian matryoshka doll: we will start from the simplest case of a Markov process (MP), then extend that with rewards, which will turn it into a Markov reward process. Then, we will put this idea into an extra envelope by adding actions, which will lead us to an MDP.

MPs and MDPs are widely used in computer science and other engineering fields. So, reading this chapter will be useful for you not only for RL contexts,...

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Authors (1)

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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