mlsanityData to 7 Oct 2026

Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition

arXiv 2403.04568 ↗First posted 7 Mar 2024cs.LG stat.MLcited by 6

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Abstract (arXiv)

We study reinforcement learning with linear function approximation, unknown transition, and adversarial losses in the bandit feedback setting. Specifically, we focus on linear mixture MDPs whose transition kernel is a linear mixture model. We propose a new algorithm that attains an widetildeO(d√HS3K + √HSAK) regret with high probability, where d is the dimension of feature mappings, S is the size of state space, A is the size of action space, H is the episode length and K is the number of episodes. Our result strictly improves the previous best-known widetildeO(dS2 √K + √HSAK) result in Zhao et al. (2023a) since H ≤ S holds by the layered MDP structure. Our advancements are primarily attributed to (i) a new least square estimator for the transition parameter that leverages the visit information of all states, as opposed to only one state in prior work, and (ii) a new self-normalized concentration tailored specifically to handle non-independent noises, originally proposed in the dynamic assortment area and firstly applied in reinforcement learning to handle correlations between different states.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 30 of its 52 references that are in the map. First posted on arXiv 7 Mar 2024.

Builds on

30 of 52 references.

References · 30

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18 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.