125 citations · 148 across the 19 of their papers we have counts for
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cs.LG2020
Reinforcement Learning with Trajectory Feedback
Yonathan Efroni, Nadav Merlis, Shie Mannor
The standard feedback model of reinforcement learning requires revealing the reward of every visited state-action pair. However, in practice, it is often the case that such frequen…
cs.LG2020★ 1 cited
Lenient Regret for Multi-Armed Bandits
Nadav Merlis, Shie Mannor
We consider the Multi-Armed Bandit (MAB) problem, where an agent sequentially chooses actions and observes rewards for the actions it took. While the majority of algorithms try to…
cs.LG2020★ 8 cited
Tight Lower Bounds for Combinatorial Multi-Armed Bandits
Nadav Merlis, Shie Mannor
The Combinatorial Multi-Armed Bandit problem is a sequential decision-making problem in which an agent selects a set of arms on each round, observes feedback for each of these arms…