41 citations · 78 across the 9 of their papers we have counts for
9 papers
Achieving Small Test Error in Mildly Overparameterized Neural Networks
Shiyu Liang, Ruoyu Sun, R. Srikant
Recent theoretical works on over-parameterized neural nets have focused on two aspects: optimization and generalization. Many existing works that study optimization and generalizat…
Optimistic Policy Iteration for MDPs with Acyclic Transient State Structure
Joseph Lubars, Anna Winnicki, Michael Livesay +1
We consider Markov Decision Processes (MDPs) in which every stationary policy induces the same graph structure for the underlying Markov chain and further, the graph has the follow…
Combining Reinforcement Learning with Model Predictive Control for On-Ramp Merging
Joseph Lubars, Harsh Gupta, Sandeep Chinchali +4
We consider the problem of designing an algorithm to allow a car to autonomously merge on to a highway from an on-ramp. Two broad classes of techniques have been proposed to solve…
The Mean-Squared Error of Double Q-Learning
Wentao Weng, Harsh Gupta, Niao He +2
In this paper, we establish a theoretical comparison between the asymptotic mean-squared error of Double Q-learning and Q-learning. Our result builds upon an analysis for linear st…
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…
Finite-Time Error Bounds For Linear Stochastic Approximation and TD Learning
R. Srikant, Lei Ying
We consider the dynamics of a linear stochastic approximation algorithm driven by Markovian noise, and derive finite-time bounds on the moments of the error, i.e., deviation of the…