5 citations · 13 across the 24 of their papers we have counts for
7 papers · 1 filter
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…
Inverse Safety Filtering: Inferring Constraints from Safety Filters for Decentralized Coordination
Minh Nguyen, Jingqi Li, Gechen Qu +1
Safe multi-agent coordination in uncertain environments can benefit from learning constraints from other agents. Implicitly communicating safety constraints through actions is a pr…
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…
From Global to Local: Hierarchical Probabilistic Verification for Reachability Learning
Ebonye Smith, Sampada Deglurkar, Jingqi Li +2
Hamilton-Jacobi (HJ) reachability provides formal safety guarantees for nonlinear systems. However, it becomes computationally intractable in high-dimensional settings, motivating…
Active Calibration of Reachable Sets Using Approximate Pick-to-Learn
Sampada Deglurkar, Ebonye Smith, Jingqi Li +1
Reachability computations that rely on approximate or learned models require calibration in order to uphold confidence about their guarantees. Calibration generally involves sampli…
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…
Generalized Information Gathering Under Dynamics Uncertainty
Fernando Palafox, Jingqi Li, Jesse Milzman +1
An agent operating in an unknown dynamical system must learn its dynamics from observations. Active information gathering accelerates this learning, but existing methods derive bes…