Transfer in reinforcement learning is a novel research area that focuses on the development of methods to transfer knowledge from a set of source tasks to a target task. Whenever the tasks are similar, the transferred knowledge can be used by a learning algorithm to solve the target task and significantly improve its performance (e.g., by reducing the number of samples needed to achieve a nearly optimal performance). In this chapter we provide a formalization of the general transfer problem, we identify the main settings which have been investigated so far, and we review the most important approaches to transfer in reinforcement learning.
CITATION STYLE
Lazaric, A. (2012). Transfer in reinforcement learning: A framework and a survey. In Adaptation, Learning, and Optimization (Vol. 12, pp. 143–173). Springer Verlag. https://doi.org/10.1007/978-3-642-27645-3_5
Mendeley helps you to discover research relevant for your work.