Deep reinforcement learning

By:

Plaat, Aske

Material type: Text

TextPublication details: Singapore Springer 2022Description: xv, 406 pISBN:

9789811906374

Subject(s):

DDC classification:

006.31 PLA

Summary: Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning. (https://link.springer.com/book/10.1007/978-981-19-0638-1)

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.31 PLA (Browse shelf(Opens below))	1	Available		009015

Table of contents:
Front Matter
Pages i-xv
Download chapter PDF
Introduction
Aske Plaat
Pages 1-24
Tabular Value-Based Reinforcement Learning
Aske Plaat
Pages 25-67
Deep Value-Based Reinforcement Learning
Aske Plaat
Pages 69-100
Policy-Based Reinforcement Learning
Aske Plaat
Pages 101-133
Model-Based Reinforcement Learning
Aske Plaat
Pages 135-167
Two-Agent Self-Play
Aske Plaat
Pages 169-218
Multi-Agent Reinforcement Learning
Aske Plaat
Pages 219-262
Hierarchical Reinforcement Learning
Aske Plaat
Pages 263-285
Meta-Learning
Aske Plaat
Pages 287-322
Further Developments
Aske Plaat
Pages 323-336

[https://link.springer.com/book/10.1007/978-981-19-0638-1]

Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence.
These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.

(https://link.springer.com/book/10.1007/978-981-19-0638-1)

There are no comments on this title.

to post a comment.