A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning

In this paper we consider the problem of how a reinforcement learning agent that is tasked with solving a sequence of reinforcement learning problems (Markov decision processes) can use knowledge acquired early in its lifetime to improve its ability to solve new problems. Specifically, we focus on the question of how the agent should explore when faced with a new environment. We show that the search for an optimal exploration strategy can be formulated as a reinforcement learning problem itself, albeit with a different timescale. We conclude with experiments that show the benefits of optimizing an exploration strategy using our proposed approach.