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Found 8 papers, 0 papers with code
Feel-Good Thompson Sampling for Contextual Dueling Bandits
In this paper, we propose a Thompson sampling algorithm, named FGTS. CDB, for linear contextual dueling bandits.
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
The exploration-exploitation dilemma has been a central challenge in reinforcement learning (RL) with complex model classes.
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning
However, limited works on offline RL with non-linear function approximation have instance-dependent regret guarantees.
Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits
Dueling bandits is a prominent framework for decision-making involving preferential feedback, a valuable feature that fits various applications involving human interaction, such as ranking, information retrieval, and recommendation systems.
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency
We propose a variance-adaptive algorithm for linear mixture MDPs, which achieves a problem-dependent horizon-free regret bound that can gracefully reduce to a nearly constant regret for deterministic MDPs.
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
We study reinforcement learning (RL) with linear function approximation.
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits
We study the problem of online generalized linear regression in the stochastic setting, where the label is generated from a generalized linear model with possibly unbounded additive noise.
Linear Contextual Bandits with Adversarial Corruptions
We study the linear contextual bandit problem in the presence of adversarial corruption, where the interaction between the player and a possibly infinite decision set is contaminated by an adversary that can corrupt the reward up to a corruption level $C$ measured by the sum of the largest alteration on rewards in each round.
