Normal view MARC view ISBD view

Advances in Reinforcement Learning

By:

Mellouk, Abdelhamid [edt]

Contributor(s):

Mellouk, Abdelhamid [oth]

Material type: Article

ArticleLanguage: English Publication details: IntechOpen 2011Description: 1 electronic resource (484 p.)ISBN:

557
9789533073699
9789535155034

Subject(s):

Online resources:

Summary: Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic.

Item type:

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

Average rating: 0.0 (0 votes)

No physical items for this record

Open Access star Unrestricted online access

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic.

Creative Commons https://creativecommons.org/licenses/by-nc-sa/3.0/ cc https://creativecommons.org/licenses/by-nc-sa/3.0/