688 citations · 1.3k across the 44 of their papers we have counts for
47 papers
Sample Efficient Hierarchical Reinforcement Learning via Best Policy Identification
Anders Jonsson, Emilie Kaufmann, Gianmarco Tedeschi +1
We present HBPI-UCRL, a model-based algorithm for hierarchical reinforcement learning (HRL) that learns high-level and low-level policies in parallel. HBPI-UCRL exploits the fact t…
Repeated Bilateral Trade: The Quest for Fairness
François Bachoc, Roberto Colomboni, Emilie Kaufmann
We study repeated bilateral trade from a fairness perspective. At each round, a fresh seller-buyer pair arrives, and the platform posts a price before observing the traders' valuat…
Power Mean Estimation in Stochastic Monte-Carlo Tree_Search
Tuan Dam, Odalric-Ambrym Maillard, Emilie Kaufmann
Monte-Carlo Tree Search (MCTS) is a widely-used strategy for online planning that combines Monte-Carlo sampling with forward tree search. Its success relies on the Upper Confidence…
Towards Instance-Optimality in Online PAC Reinforcement Learning
Aymen Al-Marjani, Andrea Tirinzoni, Emilie Kaufmann
Several recent works have proposed instance-dependent upper bounds on the number of episodes needed to identify, with probability , an -optimal policy in finite-h…
Adaptive Algorithms for Relaxed Pareto Set Identification
Cyrille Kone, Emilie Kaufmann, Laura Richert
In this paper we revisit the fixed-confidence identification of the Pareto optimal set in a multi-objective multi-armed bandit model. As the sample complexity to identify the exact…
Active Coverage for PAC Reinforcement Learning
Aymen Al-Marjani, Andrea Tirinzoni, Emilie Kaufmann
Collecting and leveraging data with good coverage properties plays a crucial role in different aspects of reinforcement learning (RL), including reward-free exploration and offline…