A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning

NeurIPS 2019 Francisco M. GarciaPhilip S. Thomas

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... (read more)

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