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University of Oxford
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Learning dense rewards from expert demonstrations allows for over 90% success in complex manipulation tasks, outperforming traditional RL methods.
GP-PSRL can achieve sublinear regret bounds in continuous control even with unbounded state spaces, resolving prior theoretical limitations and opening the door to more complex RL settings.
