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Truly No-Regret Learning in Constrained MDPs
As Efroni et al. (2020) pointed out, it is an open question whether primal-dual algorithms can provably achieve sublinear regret if we do not allow error cancellations.
Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes
Unlike existing Lagrangian approaches, our algorithm achieves this regret without the need for the cancellation of errors.
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