Tutorial by

Alessandro Lazaric (INRIA Lille) and Matthew Taylor (University of Southern California)

Friday 11 September (Room Finzgar, Hotel Park)

Abstract:

Machine learning algorithms often require a large amount of data to solve a given task, even when similar tasks have already been solved. The insight of transfer learning (TL) is that by using data from one or more related tasks, it is possible to learn the target task with less data, which may be expensive to gather. One emerging use of transfer is in conjunction with \emph{reinforcement learning} (RL). Recent works combining TL and RL show that improved data efficiency may directly translate to improved learning performance, a critical problem for sequential decision-making problems in which the collection of large amounts of data by direct interaction is unfeasible (e.g., autonomous robotics). Although the work on TL in RL provides many techniques to address a number of different problems, it is still difficult for non-specialists to select the most suitable method for a given task or RL learning algorithm. The objectives of this tutorial are threefold. First, we introduce the key goals of transfer and briefly review techniques developed in a wide range of learning settings. Second, we survey successful approaches to transfer in RL while emphasizing when and how different methods can be utilized. Third, we discuss a set of open problems and research directions. With this tutorial, we expect that participants will better be prepared to both utilize and improve upon TL methods in RL domains.