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Transfer Learning for Reinforcement Learning Domains: A Survey

Authors:

Matthew E. Taylor,

Peter StoneAuthors Info & Claims

The Journal of Machine Learning Research, Volume 10

Pages 1633 - 1685

Published: 01 December 2009 Publication History

Abstract

The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea of transfer is that experience gained in learning to perform one task can help improve learning performance in a related, but different, task. In this article we present a framework that classifies transfer learning methods in terms of their capabilities and goals, and then use it to survey the existing literature, as well as to suggest future directions for transfer learning work.

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Transfer Learning for Reinforcement Learning Domains: A Survey
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12/1/2009

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Bazzan Ade Almeida VAbdoos MBazzan ADusparic ILujak MVizzari G(2024)Transferring experiences in k-nearest neighbors based multiagent reinforcement learningAI Communications10.3233/AIC-22030537:2(247-259)Online publication date: 1-Jan-2024
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