activity
20182026
most citedInfinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning

5 citations · 13 across the 24 of their papers we have counts for

collaborators
Showing cs.GTShow all

5 papers · 1 filter

cs.GT2026

Can Play That Game: Online Fitted -Iteration for Continuous-Action Zero-Sum Markov Games with Convex-Concave Function Approximation

Kushagra Gupta, Jingqi Li, Cade Armstrong +4

Zero-sum Markov games arise in a wide variety of sequential decision-making problems such as adversarial learning and planning against modeled uncertainties. However, prior work on…

cs.GT2026

Breaking Exponential Complexity in Games of Ordered Preference: A Tractable Reformulation

Dong Ho Lee, Jingqi Li, Lasse Peters +2

Games of ordered preference (GOOPs) model multi-player equilibrium problems in which each player maintains a distinct hierarchy of strictly prioritized objectives. Existing approac…

cs.GT2026

Efficiently Solving Mixed-Hierarchy Games with Quasi-Policy Approximations

Hamzah Khan, Dong Ho Lee, Jingqi Li +5

Multi-robot coordination often exhibits hierarchical structure, with some robots' decisions depending on the planned behaviors of others. While game theory provides a principled fr…

cs.GT2025

Level-2 Inverse Games for Inferring Agents' Estimates of Others' Objectives

Hamzah I. Khan, Jingqi Li, David Fridovich-Keil

Effectively interpreting strategic interactions among multiple agents requires us to infer each agent's objective from limited information. Existing inverse game-theoretic approach…

cs.GT2024

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…