4 citations · 17 across the 9 of their papers we have counts for
Showing 2020 · cs.LGShow all
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cs.LG2020★ 2 cited
One-bit feedback is sufficient for upper confidence bound policies
Daniel Vial, Sanjay Shakkottai, R. Srikant
We consider a variant of the traditional multi-armed bandit problem in which each arm is only able to provide one-bit feedback during each pull based on its past history of rewards…
cs.LG2020★ 4 cited
Robust Multi-Agent Multi-Armed Bandits
Daniel Vial, Sanjay Shakkottai, R. Srikant
Recent works have shown that agents facing independent instances of a stochastic -armed bandit can collaborate to decrease regret. However, these works assume that each agent al…
cs.LG2020
Empirical Policy Evaluation with Supergraphs
Daniel Vial, Vijay Subramanian
We devise and analyze algorithms for the empirical policy evaluation problem in reinforcement learning. Our algorithms explore backward from high-cost states to find high-value one…