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@Article{AAAIMag11-Taylor,
  Author="Matthew E.\ Taylor and Peter Stone",
  title="An Introduction to Inter-task Transfer for Reinforcement Learning",
  journal="{AI} Magazine",
  year="2011",
  volume="32",
  number="1",
  pages="15--34",
  abstract="
            Transfer learning has recently gained popularity due to
            the development of algorithms that can successfully
            generalize information across multiple tasks. 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.",
}