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Found 7 papers, 1 papers with code
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
We also prove a lower bound to show that the additive dependence on $C$ is optimal.
Model-based Reinforcement Learning
reinforcement-learning
+1
Online Iterative Reinforcement Learning from Human Feedback with General Preference Model
We study Reinforcement Learning from Human Feedback (RLHF) under a general preference oracle.
Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint
This includes an iterative version of the Direct Preference Optimization (DPO) algorithm for online settings, and a multi-step rejection sampling strategy for offline scenarios.
Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks
For these settings, we design a provably sample-efficient algorithm which achieves a $ \mathcal{\tilde O}(s_0^2 \log^2 T)$ regret in the sparse case and $ \mathcal{\tilde O} ( r ^2 \log^2 T)$ regret in the low-rank case, using only $L = \mathcal{O}( \log T)$ batches.
Corruption-Robust Offline Reinforcement Learning with General Function Approximation
Notably, under the assumption of single policy coverage and the knowledge of $\zeta$, our proposed algorithm achieves a suboptimality bound that is worsened by an additive factor of $\mathcal{O}(\zeta (C(\widehat{\mathcal{F}},\mu)n)^{-1})$ due to the corruption.
Optimal Sample Selection Through Uncertainty Estimation and Its Application in Deep Learning
Our proposed method, COPS (unCertainty based OPtimal Sub-sampling), is designed to minimize the expected loss of a model trained on subsampled data.
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes
In this paper, we consider the contextual bandit with general function approximation and propose a computationally efficient algorithm to achieve a regret of $\tilde{O}(\sqrt{T}+\zeta)$.
