arXiv Statistics ML
Tighter Regret Bounds for Contextual Action-Set Reinforcement Learning
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
Related stories
arXiv Machine Learning
Jul 23
Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence
arXiv:2607. 19854v1 Announce Type: new Abstract: We study horizon-free regret minimization for finite-horizon time-homogeneous tabular Markov decision processes with $S$ states, $A$ actions, horizon $H$, and per-trajectory total reward bounded by $1$.
arXiv Machine Learning
4d ago
Optimal Multi-Reward Reinforcement Learning
arXiv:2609.36486v1 Announce Type: new Abstract: We study an unknown-transition finite-horizon Markov decision process (MDP) with a finite collection of known reward functions $\{r^1, r^2, \ldots, r^M...
arXiv Machine Learning
Jun 2
Online Learning in MDPs with Partially Adversarial Transitions and Losses
arXiv:2602. 09474v2 Announce Type: replace Abstract: We study reinforcement learning in MDPs whose transition function is stochastic at most steps but may behave adversarially at a fixed subset of $\Lambda$ steps per episode.
arXiv Machine Learning
Jun 25
Minimax PAC Bounds for Learning in Exogenous Contextual MDPs
arXiv:2606. 25170v1 Announce Type: cross Abstract: We study PAC learning in tabular discounted Markov decision processes with exogenous i.
arXiv Machine Learning
Jun 2
An Improved Algorithm for Adversarial Linear Contextual Bandits via Reduction
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.
arXiv Machine Learning
Aug 27
Minimax Alternating Regret for the Experts Problem and Online Convex Optimization
arXiv:2608. 25182v1 Announce Type: cross Abstract: In this paper, we study alternating regret in online convex optimization (OCO), motivated by the success of alternating learning dynamics in two-player games.