1 citations · 2 across the 8 of their papers we have counts for
8 papers
AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training
Binghang Lu, Runyu Zhang, Changhong Mou +2
Physics-informed neural networks (PINNs) provide a flexible framework for solving forward and inverse problems governed by partial differential equations (PDEs), but standard PINN…
Zeroth-Order Constrained Optimization from a Control Perspective via Feedback Linearization
Runyu Zhang, Gioele Zardini, Asuman Ozdaglar +2
Safe derivative-free optimization under unknown constraints is a fundamental challenge in modern learning and control. Existing zeroth-order (ZO) methods typically still assume acc…
Optimism as Risk-Seeking in Multi-Agent Reinforcement Learning
Runyu Zhang, Na Li, Asuman Ozdaglar +2
Risk sensitivity has become a central theme in reinforcement learning (RL), where convex risk measures and robust formulations provide principled ways to model preferences beyond e…
Constrained Optimization From a Control Perspective via Feedback Linearization
Runyu Zhang, Arvind Raghunathan, Jeff Shamma +1
Tools from control and dynamical systems have proven valuable for analyzing and developing optimization methods. In this paper, we establish rigorous theoretical foundations for us…
Equilibrium Selection for Multi-agent Reinforcement Learning: A Unified Framework
Runyu Zhang, Gioele Zardini, Asuman Ozdaglar +2
While multi-agent reinforcement learning (MARL) has produced numerous algorithms that converge to Nash or related equilibria, such equilibria are often non-unique and can exhibit w…
Multi-Agent Coverage Control with Transient Behavior Consideration
Runyu Zhang, Haitong Ma, Na Li
This paper studies the multi-agent coverage control (MAC) problem where agents must dynamically learn an unknown density function while performing coverage tasks. Unlike many curre…
Scalable Reinforcement Learning for Linear-Quadratic Control of Networks
Johan Olsson, Runyu Zhang, Emma Tegling +1
Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such probl…
Cooperative Multi-Agent Graph Bandits: UCB Algorithm and Regret Analysis
Phevos Paschalidis, Runyu Zhang, Na Li
In this paper, we formulate the multi-agent graph bandit problem as a multi-agent extension of the graph bandit problem introduced by Zhang, Johansson, and Li [CISS 57, 1-6 (2023)]…