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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…