3 citations · 3 across the 9 of their papers we have counts for
7 papers · 1 filter
Provably Efficient Exploration in Reward Machines with Low Regret
Hippolyte Bourel, Anders Jonsson, Odalric-Ambrym Maillard +2
We study reinforcement learning (RL) for decision processes with non-Markovian reward, in which high-level knowledge of the task in the form of reward machines is available to the…
Near-Optimal Reinforcement Learning with Shuffle Differential Privacy
Shaojie Bai, Mohammad Sadegh Talebi, Chengcheng Zhao +2
Reinforcement learning (RL) is a powerful tool for sequential decision-making, but its application is often hindered by privacy concerns arising from its interaction data. This cha…
Tractable Offline Learning of Regular Decision Processes
Ahana Deb, Roberto Cipollone, Anders Jonsson +2
This work studies offline Reinforcement Learning (RL) in a class of non-Markovian environments called Regular Decision Processes (RDPs). In RDPs, the unknown dependency of future o…
Improved Exploration in Factored Average-Reward MDPs
Mohammad Sadegh Talebi, Anders Jonsson, Odalric-Ambrym Maillard
We consider a regret minimization task under the average-reward criterion in an unknown Factored Markov Decision Process (FMDP). More specifically, we consider an FMDP where the st…
Tightening Exploration in Upper Confidence Reinforcement Learning
Hippolyte Bourel, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi
The upper confidence reinforcement learning (UCRL2) algorithm introduced in (Jaksch et al., 2010) is a popular method to perform regret minimization in unknown discrete Markov Deci…
Model-Based Reinforcement Learning Exploiting State-Action Equivalence
Mahsa Asadi, Mohammad Sadegh Talebi, Hippolyte Bourel +1
Leveraging an equivalence property in the state-space of a Markov Decision Process (MDP) has been investigated in several studies. This paper studies equivalence structure in the r…