The paper addresses policy optimization in online episodic tabular MDPs with adversarial losses and bandit feedback. By employing regularized Q‑functions, the authors eliminate the extra horizon factor present in previous policy‑optimization regret bounds, achieving high‑probability regret of ≥O(√{HS(H+A)T}) for known transitions and ≥O(HS√{AT}) for unknown transitions. These results improve horizon dependence and match the best-known bounds, with extensions to adversarial linear‑mixture MDPs.
By Mingyi Li, Taira Tsuchiya
arXiv:2510. 07424v3 Announce Type: replace Abstract: We study linear contextual bandits with paid observations, where at each round the learner observes a context, selects an action, and may pay a fixed cost to observe feedback from a subset of arms.
By Nathan Boyer, Dorian Baudry, Patrick Rebeschini
arXiv:2508. 11931v3 Announce Type: replace Abstract: We present an oracle-efficient, near-optimal algorithm for linear contextual bandits with adversarial losses and stochastic action sets, only requiring a linear optimization oracle for the action sets in each round.
By Tim van Erven, Jack Mayo, Julia Olkhovskaya, Chen-Yu Wei
arXiv:2602. 09456v2 Announce Type: replace Abstract: We propose an algorithmic framework, Offline Estimation to Decisions (OE2D), that efficiently reduces contextual bandit learning with general reward function approximation to offline regression.
By Hao Qin, Chicheng Zhang
Meta-LinEXP3 is an online-within-online algorithm designed for adversarial linear contextual bandits with random action sets. It builds a task-level prior from completed tasks to guide an inner LinEXP3 learner, achieving an σO(√n) per‑task regret when context distributions are known and an σO(n^{2/3}) regret with a past‑only regularized moment estimator when they are unknown. The paper also links prior accuracy to transfer regret, showing that better priors yield sublinear, transfer‑dependent regret across tasks, and demonstrates the method on structured hyperspectral tensor sampling.
By Hao Li, Jie Xu, Zheng Xie
The paper extends dueling bandits to episodic Markov decision processes where selecting a pair of arms not only yields a noisy preference but also influences the next state. It defines a state‑wise preference‑based MDP with adversarially changing preferences and Borda‑score losses, and proves a minimax regret of ∫Θ(min{HT,(H^3SK)^{1/3}T^{2/3}}) under known transitions. The authors provide an online linear‑optimization algorithm achieving this bound, a policy‑optimization algorithm with closed‑form updates, and extensions to unknown transitions.
By Taira Tsuchiya, Shinji Ito, Haipeng Luo