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01/06/2022
Gaussian Imagination in Bandit Learning
Assuming distributions are Gaussian often facilitates computations that ...
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02/18/2021
A Bit Better? Quantifying Information for Bandit Learning
The information ratio offers an approach to assessing the efficacy with ...
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02/24/2020
Q-learning with Uniformly Bounded Variance: Large Discounting is Not a Barrier to Fast Learning
It has been a trend in the Reinforcement Learning literature to derive s...
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02/07/2020
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation
This paper concerns error bounds for recursive equations subject to Mark...
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10/11/2019
Zap Q-Learning With Nonlinear Function Approximation
The Zap stochastic approximation (SA) algorithm was introduced recently ...
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07/22/2019
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization
We consider a generic empirical composition optimization problem, where ...
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04/25/2019
Zap Q-Learning for Optimal Stopping Time Problems
We propose a novel reinforcement learning algorithm that approximates so...
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12/28/2018
Differential Temporal Difference Learning
Value functions derived from Markov decision processes arise as a centra...
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09/17/2018
