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Hippolyte Bourel

2 accepted papers

2023

Exploration in Reward Machines with Low Regret

AISTATS 2023poster

We study reinforcement learning (RL) for decision processes with non-Markovian reward, in which high-level knowledge in the form of reward machines is available to the learner. Specifically, we investigate the efficiency of RL under the average-reward criterion, in the regret minimization setting. W…

Cited by 11Source ↗PDF ↗Scholar ↗
2020

Tightening Exploration in Upper Confidence Reinforcement Learning

ICML 2020poster

The upper confidence reinforcement learning (UCRL2) algorithm introduced in \citep{jaksch2010near} is a popular method to perform regret minimization in unknown discrete Markov Decision Processes under the average-reward criterion. Despite its nice and generic theoretical regret guarantees, this alg…

Cited by 50Source ↗PDF ↗Scholar ↗