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

Product typeBook

Published inJan 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781838826994

Edition2nd Edition

Languages

Python

Tools

Keras TensorFlow

Concepts

Deep Reinforcement Learning

Author (1)

Maxim Lapan

The AlphaGo Zero method

In this section, we will discuss the structure of the method. The whole system contains several parts that need to be understood before we can implement them.

Overview

At a high level, the method consists of three components, all of which will be explained in detail later, so don't worry if something is not completely clear from this section:

We constantly traverse the game tree using the Monte Carlo tree search (MCTS) algorithm, the core idea of which is to semi-randomly walk down the game states, expanding them and gathering statistics about the frequency of moves and underlying game outcomes. As the game tree is huge, both in terms of the depth and width, we don't try to build the full tree; we just randomly sample its most promising paths (that's the source of the method's name).
At every moment, we have a best player, which is the model used to generate the data via self-play. Initially, this model has random weights...

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Deep Reinforcement Learning Hands-On. - Second Edition

Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994

Author (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.
Read more about Maxim Lapan

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

The AlphaGo Zero method

Overview

Unlock this book and the full library FREE for 7 days

Author (1)

TensorFlow Reinforcement Learning Quick Start Guide

Deep Reinforcement Learning with Python

Hands-On Intelligent Agents with OpenAI Gym

Hands-On Q-Learning with Python

PyTorch 1.x Reinforcement Learning Cookbook

TensorFlow 2 Reinforcement Learning Cookbook

Reinforcement Learning Algorithms with Python

Reinforcement Learning with TensorFlow

Hands-On Reinforcement Learning for Games

Mastering Reinforcement Learning with Python

Hands-On Reinforcement Learning with Python

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