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Entropy regularization makes planning provably easy: SmoothCruiser achieves polynomial sample complexity in MDPs where standard methods fail.
Learning user preferences for thousands of items can be achieved with just a handful of evaluations, thanks to a novel approach that leverages effective dimension in graph-based bandit problems.
TrailBlazer offers a computationally efficient Monte-Carlo planning algorithm that drastically reduces sample complexity by focusing exploration on near-optimal state trajectories within an MDP.
