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.
Keywords
Transfer of learningReinforcement learningComputer scienceTask (project management)Inductive transferMulti-task learningArtificial intelligenceActive learning (machine learning)Error-driven learningMachine learningCore (optical fiber)Robot learningEngineering
Affiliated Institutions
Related Publications
Meta-Transfer Learning for Few-Shot Learning
Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in orde...
A Survey on Policy Search for Robotics
<p>Policy search is a subfield in reinforcement learning which focuses onfinding good parameters for a given policy parametrization. It is wellsuited for robotics as it ca...
An Introduction to Deep Reinforcement Learning
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-m...
A Survey on Multi-Task Learning
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the genera...
Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications
Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms, however, have faced grea...
Publication Info
- Year
- 2009
- Type
- article
- Volume
- 10
- Issue
- 56
- Pages
- 1633-1685
- Citations
- 1561
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
1561
OpenAlex
Cite This
Matthew E. Taylor,
Peter Stone
(2009).
Transfer Learning for Reinforcement Learning Domains: A Survey.
Journal of Machine Learning Research
, 10
(56)
, 1633-1685.
https://doi.org/10.5555/1577069.1755839
Identifiers
- DOI
- 10.5555/1577069.1755839