Transfer Learning for Multiagent Reinforcement Learning Systems

Language: English

Published by Springer, Springer Mai 2021, 2021

3031004639 / 9783031004636

  • Softcover
  • New
See all details

Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000

5-star seller

AbeBooks seller since January 23, 2017

Softcover

Condition: New

US$ 74.14

US$ 67.28 shipping 
Ships from Germany to U.S.A.

Quantity: 1 available

Add to basket
Free 30-day returns

Item description from seller

This item is printed on demand - Print on Demand Titel. Neuware -Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is a well-known technique to train autonomous agents through interactions with the environment. Unfortunately, the learning process has a high sample complexity to infer an effective actuation policy, especially when multiple agents are simultaneously actuating in the environment.However, previous knowledge can be leveraged to accelerate learning and enable solving harder tasks. In the same way humans build skills and reuse them by relating different tasks, RL agents might reuse knowledge from previously solved tasks and from the exchange of knowledge with other agents in the environment. In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning.This book surveys the literature on knowledge reuse in multiagent RL. The authors define a unifying taxonomy of state-of-the-art solutions for reusing knowledge, providing a comprehensive discussion of recent progress in the area. In this book, readers will find a comprehensive discussion of the many ways in which knowledge can be reused in multiagent sequential decision-making tasks, as well as in which scenarios each of the approaches is more efficient. The authors also provide their view of the current low-hanging fruit developments of the area, as well as the still-open big questions that could result in breakthrough developments. Finally, the book provides resources to researchers who intend to join this area or leverage those techniques, including a list of conferences, journals, and implementation tools.This book will be useful for a wide audience; and will hopefully promote new dialogues across communities and novel developments in the area.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 132 pp. Englisch. …

Seller Inventory # 9783031004636

Title
Transfer Learning for Multiagent Reinforcement Learning Systems
Author
Felipe Leno Da Silva
Publisher
Springer, Springer Mai 2021
Publication year
2021
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3031004639
ISBN 13
9783031004636
Item weight
262 grams
Dimensions
235x191x8 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

5-star seller

AbeBooks seller since January 23, 2017

Shipping rates from Germany to U.S.A.

Item60 to 60 business days60 to 60 business days
First itemUS$ 67.28US$ 84.10
Delivery times are set by sellers and vary by carrier and location. Orders passing through Customs may face delays and buyers are responsible for any associated duties or fees. Sellers may contact you regarding additional charges to cover any increased costs to ship your items.

Payment methods

  • Visa
  • Mastercard
  • American Express
  • Apple Pay
  • Google Pay
  • Check
  • Paypal

Store description

Impressum Thorsten Retsch Buchversand Mimpf2000 Oberölschnitz 16 95517 Emtmannsberg Deutschland Telefon: 09209-2023188 Email: mimpf2000@online.de USt-ID-Nr.: DE 235096871 Wir führen gebrauchte Bücher aus allen Sparten der Literatur

Specialty

Modernes Antiquariat - Bücher von 1960 bis heute

Seller's business information

buchversandmimpf2000

Germany