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  • title: Cyclophobic Reinforcement Learning
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            Cyclophobic Reinforcement Learning
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            Cyclophobic Reinforcement Learning

            Dec 2, 2022

            Speakers

            SW

            Stefan Wagner

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            PA

            Peter Arndt

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            JR

            Jan Robine

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            About

            In environments with sparse rewards finding a good inductive bias for exploration is crucial to the agent’s success. However, there are two competing goals: novelty search and systematic exploration. While existing approaches such as curiousity-driven exploration find novelty, they sometimes do not systematically explore the whole state space, akin to depth-first-search vs breadth-first-search. In this paper, we propose a new intrinsic reward that is cyclophobic, i.e. it does not reward novelty,…

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            NeurIPS 2022

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