research
∙
06/09/2023
Functional Central Limit Theorem for Two Timescale Stochastic Approximation
Two time scale stochastic approximation algorithms emulate singularly pe...
share
research
∙
04/07/2023
Full Gradient Deep Reinforcement Learning for Average-Reward Criterion
We extend the provably convergent Full Gradient DQN algorithm for discou...
research
∙
10/09/2022
A Concentration Bound for Distributed Stochastic Approximation
We revisit the classical model of Tsitsiklis, Bertsekas and Athans for d...
research
∙
06/07/2022
Concentration bounds for SSP Q-learning for average cost MDPs
We derive a concentration bound for a Q-learning algorithm for average c...
research
∙
10/27/2021
The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning
The paper concerns convergence and asymptotic statistics for stochastic ...
research
∙
02/15/2021
A Unified Batch Selection Policy for Active Metric Learning
Active metric learning is the problem of incrementally selecting high-ut...
research
∙
12/21/2019
Online Reinforcement Learning of Optimal Threshold Policies for Markov Decision Processes
Markov Decision Process (MDP) problems can be solved using Dynamic Progr...
research
∙
10/19/2019
Opinion shaping in social networks using reinforcement learning
In this paper, we study how to shape opinions in social networks when th...
research
∙
11/28/2018
