  In this paper we consider the problem of how a reinforcement learning agent
that is tasked with solving a sequence of reinforcement learning problems (a
sequence of Markov decision processes) can use knowledge acquired early in its
lifetime to improve its ability to solve new problems. We argue that previous
experience with similar problems can provide an agent with information about
how it should explore when facing a new but related problem. We show that the
search for an optimal exploration strategy can be formulated as a reinforcement
learning problem itself and demonstrate that such strategy can leverage
patterns found in the structure of related problems. We conclude with
experiments that show the benefits of optimizing an exploration strategy using
our proposed approach.
