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Found 4 papers, 0 papers with code
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback
Learning from human feedback plays an important role in aligning generative models, such as large language models (LLM).
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path
Our algorithm achieves an $\tilde{\mathcal O}(dB_*\sqrt{K})$ regret bound, where $d$ is the dimension of the feature mapping in the linear transition kernel, $B_*$ is the upper bound of the total cumulative cost for the optimal policy, and $K$ is the number of episodes.
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.
