… similar, the transferred knowledge can be used by a learning … a formalization of the general
transfer problem, we identify the … important approaches to transfer in reinforcement learning. …

… Transfer in reinforcement learning refers to the notion that generalization should occur not
only within a task but also across tasks. We propose a transfer … the reinforcement learning …

… transfer in reinforcement learning based on the idea that related tasks share some common
features, and that transfer … provides some insight into when transfer can be usefully applied …

… to learn a target task faster. Recently introduced transfer methods in reinforcement learning
settings have shown considerable promise, but they typically transfer between pairs of very …

Recent work in transfer learning has succeeded in making reinforcement learning algorithms
more efficient by incorporating knowledge from previous tasks. However, such methods …

… Domain adaptation is an important open problem in deep reinforcement learning (RL). In
many scenarios of interest data is hard to obtain, so agents may learn a source policy in a …

… Reinforcement Learning Background This monograph focuses on transfer learning in
reinforcement learning domains; some RL background is necessary. Our goal in this chapter is to …

… In this article we present a framework that classifies transfer learning methods in terms of
their … Transfer Figure 1: This article focuses on transfer between reinforcement learning tasks. …

… -of-the-art transfer learning approaches, under which we analyze … reinforcement learning
backbones, and practical applications. We also draw connections between transfer learning …

… learning on the robot. To this end, we introduce a novel reinforcement learning task for
humanoid robots and demonstrate that transfer learning can be effective for this task. The results …