1 citations · 2 across the 9 of their papers we have counts for
11 papers
Vanilla Policy Optimization Is Both Optimal and Differentially Private for Stochastic Contextual Bandits
Idan Attias, Orin Levy, Alexander Ryabchenko +2
Can vanilla policy optimization explore enough to achieve near-optimal regret in stochastic contextual bandits? We show that standard exponential policy updates driven by offline r…
Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation
Orin Levy, Aviv Rosenberg, Alon Cohen +1
We introduce \texttt{OPO-CMDP}, the first policy optimization algorithm for stochastic Contextual Markov Decision Process (CMDPs) under general offline function approximation. Our…
Optimal Regret for Policy Optimization in Contextual Bandits
Orin Levy, Yishay Mansour
We present the first high-probability optimal regret bound for a policy optimization technique applied to the problem of stochastic contextual multi-armed bandit (CMAB) with genera…
The Horizon Threshold in Cooperative Multi-Agent Reward-Free Exploration
Idan Barnea, Orin Levy, Yishay Mansour
We study cooperative multi-agent reinforcement learning in the setting of reward-free exploration, where multiple agents jointly explore an unknown MDP in order to learn its dynami…
Regret Bounds for Adversarial Contextual Bandits with General Function Approximation and Delayed Feedback
Orin Levy, Liad Erez, Alon Cohen +1
We present regret minimization algorithms for the contextual multi-armed bandit (CMAB) problem over actions in the presence of delayed feedback, a scenario where loss observati…
Online Weighted Paging with Unknown Weights
Orin Levy, Noam Touitou, Aviv Rosenberg
Online paging is a fundamental problem in the field of online algorithms, in which one maintains a cache of slots as requests for fetching pages arrive online. In the weighted…
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits
Asaf Cassel, Orin Levy, Yishay Mansour
Efficiently trading off exploration and exploitation is one of the key challenges in online Reinforcement Learning (RL). Most works achieve this by carefully estimating the model u…
Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation
Orin Levy, Alon Cohen, Asaf Cassel +1
We present the OMG-CMDP! algorithm for regret minimization in adversarial Contextual MDPs. The algorithm operates under the minimal assumptions of realizable function class and acc…
Eluder-based Regret for Stochastic Contextual MDPs
Orin Levy, Asaf Cassel, Alon Cohen +1
We present the E-UCRL algorithm for regret minimization in Stochastic Contextual Markov Decision Processes (CMDPs). The algorithm operates under the minimal assumptions of real…
Optimism in Face of a Context: Regret Guarantees for Stochastic Contextual MDP
Orin Levy, Yishay Mansour
We present regret minimization algorithms for stochastic contextual MDPs under minimum reachability assumption, using an access to an offline least square regression oracle. We ana…
Learning Efficiently Function Approximation for Contextual MDP
Orin Levy, Yishay Mansour
We study learning contextual MDPs using a function approximation for both the rewards and the dynamics. We consider both the case that the dynamics dependent or independent of the…