From the 1 of 15 linked papers with an AI index.
7 papers · 1 filter
Efficient Q-Learning and Actor-Critic Methods for Robust Average-Reward Reinforcement Learning
Yang Xu, Swetha Ganesh, Vaneet Aggarwal
The paper develops and analyzes model‑free Q‑learning and actor‑critic algorithms that provably learn robust policies for infinite‑horizon average‑reward MDPs under various uncerta…
Online Bayesian Calibration under Gradual and Abrupt System Changes
Yang Xu, Chiwoo Park
Bayesian model calibration is central to digital twins and computer experiments, as it aligns model outputs with field observations by estimating calibration parameters and correct…
SAT: Sequential Agent Tuning for Coordinator Free Plug and Play Multi-LLM Training with Monotonic Improvement Guarantees
Yi Xie, Yangyang Xu, Yi Fan +1
Large language models (LLMs) with a large number of parameters achieve strong performance but are often prohibitively expensive to deploy. Recent work explores using teams of small…
Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds
Jiefu Zhang, Yang Xu, Vaneet Aggarwal
Navigating safely through dense crowds requires collision avoidance that generalizes beyond the densities seen during training. Learning-based crowd navigation can break under out-…
Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm
Yang Xu, Swetha Ganesh, Washim Uddin Mondal +2
This paper investigates infinite-horizon average reward Constrained Markov Decision Processes (CMDPs) with general parametrization. We propose a Primal-Dual Natural Actor-Critic al…
Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes
Bhargav Ganguly, Yang Xu, Vaneet Aggarwal
This paper investigates the potential of quantum acceleration in addressing infinite horizon Markov Decision Processes (MDPs) to enhance average reward outcomes. We introduce an in…