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most citedFinite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning

19 citations · 20 across the 2 of their papers we have counts for

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cs.LG2019★ 19 cited

Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning

Harsh Gupta, R. Srikant, Lei Ying

We study two time-scale linear stochastic approximation algorithms, which can be used to model well-known reinforcement learning algorithms such as GTD, GTD2, and TDC. We present f…

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…