3 papers
cs.LG2026
Bias-Controlled Primal-Dual Natural Actor-Critic: Optimal Rates for Constrained Multi-Objective Average-Reward RL
Ankur Naskar, Swetha Ganesh, Vaneet Aggarwal
Many reinforcement learning (RL) problems in the infinite-horizon average-reward setting require optimizing multiple conflicting objectives while satisfying multiple safety constra…
cs.LG2025
Parameter-Free Federated TD Learning with Markov Noise in Heterogeneous Environments
Ankur Naskar, Gugan Thoppe, Utsav Negi +1
Federated learning (FL) can dramatically speed up reinforcement learning by distributing exploration and training across multiple agents. It can guarantee an optimal convergence ra…
cs.LG2025
Parameter-free Optimal Rates for Nonlinear Semi-Norm Contractions with Applications to -Learning
Ankur Naskar, Gugan Thoppe, Vijay Gupta
Algorithms for solving \textit{nonlinear} fixed-point equations -- such as average-reward \textit{-learning} and \textit{TD-learning} -- often involve semi-norm contractions. Ac…