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cs.LG2024
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback
Asaf Cassel, Haipeng Luo, Aviv Rosenberg +1
In many real-world applications, it is hard to provide a reward signal in each step of a Reinforcement Learning (RL) process and more natural to give feedback when an episode ends.…
cs.LG2023
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback
Tal Lancewicki, Aviv Rosenberg, Dmitry Sotnikov
Policy Optimization (PO) is one of the most popular methods in Reinforcement Learning (RL). Thus, theoretical guarantees for PO algorithms have become especially important to the R…