5 papers
Near-Optimal Reinforcement Learning with Multi-Step Transition Lookahead
Corentin Pla, Hugo Richard, Marc Abeille +1
We study reinforcement learning (RL) with transition look-ahead, where the agent may observe which states would be visited upon playing any sequence of actions before deciding its…
Minimax PAC Bounds for Learning in Exogenous Contextual MDPs
Corentin Pla, Hugo Richard, Marc Abeille +1
We introduce a PAC framework in which the learner can access sampling oracles both before and at decision time. Sample complexity is measured by a pair , where is the le…
Learning to Allocate Resources with Censored Feedback
Giovanni Montanari, Côme Fiegel, Corentin Pla +2
We study an online resource allocation problem in which at each round, a budget must be allocated across arms under censored feedback. An arm yields a reward if and only if…
On the Hardness of Reinforcement Learning with Transition Look-Ahead
Corentin Pla, Hugo Richard, Marc Abeille +2
We study reinforcement learning (RL) with transition look-ahead, where the agent may observe which states would be visited upon playing any sequence of actions before decidi…
Distribution-Aware Mean Estimation under User-level Local Differential Privacy
Corentin Pla, Hugo Richard, Maxime Vono
We consider the problem of mean estimation under user-level local differential privacy, where users are contributing through their local pool of data samples. Previous work ass…