An Introduction to Intertask Transfer for Reinforcement Learning

This article focuses on transfer in the context of reinforcement learning domains, a general learning framework where an agent acts in an environment to maximize a reward signal. The goals of this article are to (1) familiarize readers with the transfer learning problem in reinforcement learning domains, (2) explain why the problem is both interesting and difficult, (3) present a selection of existing techniques that demonstrate different solutions, and (4) provide representative open problems in the hope of encouraging additional research in this exciting area. However, if agents are to behave intelligently in complex, dynamic, and noisy environments, we believe that they must be able to learn and adapt. The reinforcement learning (RL) paradigm is a popular way for such agents to learn from experience with minimal feedback. One of the central questions in RL is how best to generalize knowledge to successfully learn and adapt.