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• W2947037594 abstract "Standard reinforcement learning methods aim to master one way of solving a task whereas there may exist multiple near-optimal policies. Being able to identify this collection of near-optimal policies can allow a domain expert to efficiently explore the space of reasonable solutions. Unfortunately, existing approaches that quantify uncertainty over policies are not ultimately relevant to finding policies with qualitatively distinct behaviors. In this work, we formalize the difference between policies as a difference between the distribution of trajectories induced by each policy, which encourages diversity with respect to both state visitation and action choices. We derive a gradient-based optimization technique that can be combined with existing policy gradient methods to now identify diverse collections of well-performing policies. We demonstrate our approach on benchmarks and a healthcare task." @default.
• W2947037594 created "2019-06-07" @default.
• W2947037594 creator A5031617463 @default.
• W2947037594 creator A5038771285 @default.
• W2947037594 date "2019-05-31" @default.
• W2947037594 modified "2023-09-23" @default.
• W2947037594 title "Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies" @default.
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• W2947037594 hasPublicationYear "2019" @default.
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