2 citations · 3 across the 4 of their papers we have counts for
4 papers
Multi Timescale Stochastic Approximation: Stability and Convergence
Rohan Deb, Swetha Ganesh, Shalabh Bhatnagar
This paper presents the first sufficient conditions that guarantee the stability and almost sure convergence of multi-timescale stochastic approximation (SA) iterates. It extends t…
Schedule Based Temporal Difference Algorithms
Rohan Deb, Meet Gandhi, Shalabh Bhatnagar
Learning the value function of a given policy from data samples is an important problem in Reinforcement Learning. TD() is a popular class of algorithms to solve this problem. H…
Gradient Temporal Difference with Momentum: Stability and Convergence
Rohan Deb, Shalabh Bhatnagar
Gradient temporal difference (Gradient TD) algorithms are a popular class of stochastic approximation (SA) algorithms used for policy evaluation in reinforcement learning. Here, we…
Does Momentum Help? A Sample Complexity Analysis
Swetha Ganesh, Rohan Deb, Gugan Thoppe +1
Stochastic Heavy Ball (SHB) and Nesterov's Accelerated Stochastic Gradient (ASG) are popular momentum methods in stochastic optimization. While benefits of such acceleration ideas…