118 citations · 148 across the 21 of their papers we have counts for
23 papers
Near-Optimal Sample Complexity for Recursive Entropic Risk Reinforcement Learning with a Generative Model
Amirparsa Bahrami, Oliver Mortensen, Mohammad Sadegh Talebi
In this paper, we study the sample complexities of value and policy learning in finite discounted Markov decision processes (MDPs) under recursive entropic risk preferences with ri…
Randomized Least Squares Value Iteration itself is Joint Differentially Private
Haiyang Lu, Pratik Gajane, Shaojie Bai +1
As reinforcement learning (RL) increasingly applies to sensitive domains, such as health care and recommendation systems, privacy-preserving techniques have become essential to pro…
On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalents
Oliver Mortensen, Mohammad Sadegh Talebi
We study risk-sensitive reinforcement learning in finite discounted MDPs, where a generative model of the MDP is assumed to be available. We consider a family or risk measures call…
Recursive Entropic Risk Optimization in Discounted MDPs: Sample Complexity Bounds with a Generative Model
Oliver Mortensen, Mohammad Sadegh Talebi
We study risk-sensitive reinforcement learning in finite discounted MDPs with recursive entropic risk measures (ERM), where the risk parameter controls the agent's risk a…
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…