A robust variant of binary search achieves regret O(C + log T) for dynamic pricing with known corruption C and O(C + log² T) when unknown.
A Provably-Efficient Model-Free Algorithm for Constrained Markov Decision Processes , publisher =
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RHC-UCRL is the first algorithm for safety-constrained RL under explicit adversarial dynamics, providing sub-linear regret and constraint violation guarantees by maintaining optimism over both agent and adversary policies.
An algorithm for online resource allocation with budget and general constraints achieves O(sqrt(T)) regret in stochastic and alpha-regret in adversarial regimes with bounded constraint violations.
Presents the first algorithm to identify an ε-optimal policy in robust constrained MDPs via epigraph form and bisection search with Õ(ε^{-4}) robust policy evaluations.
citing papers explorer
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Toward Optimal Regret in Robust Pricing: Decoupling Corruption and Time
A robust variant of binary search achieves regret O(C + log T) for dynamic pricing with known corruption C and O(C + log² T) when unknown.
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Optimistic Policy Learning under Pessimistic Adversaries with Regret and Violation Guarantees
RHC-UCRL is the first algorithm for safety-constrained RL under explicit adversarial dynamics, providing sub-linear regret and constraint violation guarantees by maintaining optimism over both agent and adversary policies.
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Online Resource Allocation With General Constraints
An algorithm for online resource allocation with budget and general constraints achieves O(sqrt(T)) regret in stochastic and alpha-regret in adversarial regimes with bounded constraint violations.
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Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form
Presents the first algorithm to identify an ε-optimal policy in robust constrained MDPs via epigraph form and bisection search with Õ(ε^{-4}) robust policy evaluations.