5 citations · 13 across the 24 of their papers we have counts for
6 papers · 1 filter
Approximate Feedback Nash Equilibria with Sparse Inter-Agent Dependencies
Xinjie Liu, Jingqi Li, Filippos Fotiadis +4
Feedback Nash equilibrium strategies in multi-agent dynamic games require availability of all players' state information to compute control actions. However, in real-world scenario…
Deep Reinforcement Learning for Reach-Avoid-Stay Problems
Gabriel Chenevert, Jingqi Li, Achyuta kannan +2
Reach-Avoid-Stay (RAS) tasks are essential in applications where systems must safely reach a target set and remain within it under all bounded disturbances. Existing approaches eit…
To What Extent do Open-loop and Feedback Nash Equilibria Diverge in General-Sum Linear Quadratic Dynamic Games?
Chih-Yuan Chiu, Jingqi Li, Maulik Bhatt +1
Dynamic games offer a versatile framework for modeling the evolving interactions of strategic agents, whose steady-state behavior can be captured by the Nash equilibria of the game…
Certifiable Reachability Learning Using a New Lipschitz Continuous Value Function
Jingqi Li, Donggun Lee, Jaewon Lee +3
We propose a new reachability learning framework for high-dimensional nonlinear systems, focusing on reach-avoid problems. These problems require computing the reach-avoid set, whi…
Intent Demonstration in General-Sum Dynamic Games via Iterative Linear-Quadratic Approximations
Jingqi Li, Anand Siththaranjan, Somayeh Sojoudi +2
Autonomous agents should coordinate effectively without prior knowledge of others' intents. While prior work has focused on intent inference, we address the inverse problem: how ag…
The computation of approximate feedback Stackelberg equilibria in multi-player nonlinear constrained dynamic games
Jingqi Li, Somayeh Sojoudi, Claire Tomlin +1
Solving feedback Stackelberg games with nonlinear dynamics and coupled constraints, a common scenario in practice, presents significant challenges. This work introduces an efficien…