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  • title: Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes (ACNO-MDPs)
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            Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes (ACNO-MDPs)
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            Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes (ACNO-MDPs)

            Dec 6, 2021

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            HyunJi Nam

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            Scott Fleming

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            Emma Brunskill

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            About

            Many real-world problems that require making optimal sequences of decisions under uncertainty involve costs when the agent wishes to obtain information about its environment. We design and analyze algorithms for reinforcement learning (RL) in Action-Contingent Noiselessly Observable MDPs (ACNO-MDPs), a special class of POMDPs in which the agent can choose to either (1) fully observe the state at a cost and then act; or (2) act without any immediate observation information, relying on past observ…

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            NeurIPS 2021

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