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• W2912757816 abstract "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." @default.
• W2912757816 created "2019-02-21" @default.
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• W2912757816 date "2019-02-03" @default.
• W2912757816 modified "2023-10-12" @default.
• W2912757816 title "A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning" @default.
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• W2912757816 hasPublicationYear "2019" @default.
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