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Found 6 papers, 0 papers with code
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
This led Chen and Jiang (2019) to conjecture that concentrability (the most standard notion of coverage) and realizability (the weakest representation condition) alone are not sufficient for sample-efficient offline RL.
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
In the classical multi-armed bandit problem, instance-dependent algorithms attain improved performance on "easy" problems with a gap between the best and second-best arm.
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability
We consider the general (stochastic) contextual bandit problem under the realizability assumption, i. e., the expected reward, as a function of contexts and actions, belongs to a general function class $\mathcal{F}$.
Blind Network Revenue Management and Bandits with Knapsacks under Limited Switches
Our work reveals a surprising result: the optimal regret rate is completely characterized by a piecewise-constant function of the switching budget, which further depends on the number of resource constraints -- to the best of our knowledge, this is the first time the number of resources constraints is shown to play a fundamental role in determining the statistical complexity of online learning problems.
Online Pricing with Offline Data: Phase Transition and Inverse Square Law
We study a single-product dynamic pricing problem over a selling horizon of $T$ periods.
Phase Transitions in Bandits with Switching Constraints
We consider the classical stochastic multi-armed bandit problem with a constraint that limits the total cost incurred by switching between actions to be no larger than a given switching budget.
