29 citations · 85 across the 23 of their papers we have counts for
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cs.LG2023★ 3 cited
Restless Bandits with Average Reward: Breaking the Uniform Global Attractor Assumption
Yige Hong, Qiaomin Xie, Yudong Chen +1
We study the infinite-horizon restless bandit problem with the average reward criterion, in both discrete-time and continuous-time settings. A fundamental goal is to efficiently co…
cs.LG2023★ 2 cited
Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
Honghao Wei, Xin Liu, Weina Wang +1
This paper considers a class of reinforcement learning problems, which involve systems with two types of states: stochastic and pseudo-stochastic. In such systems, stochastic state…
cs.LG2019★ 1 cited
Almost Boltzmann Exploration
Harsh Gupta, Seo Taek Kong, R. Srikant +1
Boltzmann exploration is widely used in reinforcement learning to provide a trade-off between exploration and exploitation. Recently, in (Cesa-Bianchi et al., 2017) it has been sho…