5 citations · 13 across the 24 of their papers we have counts for
26 papers
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…
Multi-Agent Guided Policy Search for Non-Cooperative Dynamic Games
Jingqi Li, Gechen Qu, Jason J. Choi +2
Multi-agent reinforcement learning (MARL) optimizes strategic interactions in non-cooperative dynamic games, where agents have misaligned objectives. However, data-driven methods s…
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…
Resolving Conflicting Constraints in Multi-Agent Reinforcement Learning with Layered Safety
Jason J. Choi, Jasmine Jerry Aloor, Jingqi Li +3
Preventing collisions in multi-robot navigation is crucial for deployment. This requirement hinders the use of learning-based approaches, such as multi-agent reinforcement learning…
Coordinating Distributed Energy Resources with Nodal Pricing in Distribution Networks: a Game-Theoretic Approach
Eli Brock, Jingqi Li, Javad Lavaei +1
We propose a real-time nodal pricing mechanism for cost minimization and voltage control in a distribution network with autonomous distributed energy resources and analyze the resu…
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…
Evolutionary Games on Infinite Strategy Sets: Convergence to Nash Equilibria via Dissipativity
Brendon G. Anderson, Jingqi Li, Somayeh Sojoudi +1
We consider evolutionary dynamics for population games in which players have a continuum of strategies at their disposal. Models in this setting amount to infinite-dimensional diff…
Constraint Inference in Control Tasks from Expert Demonstrations via Inverse Optimization
Dimitris Papadimitriou, Jingqi Li
Inferring unknown constraints is a challenging and crucial problem in many robotics applications. When only expert demonstrations are available, it becomes essential to infer the u…
Scenario-Game ADMM: A Parallelized Scenario-Based Solver for Stochastic Noncooperative Games
Jingqi Li, Chih-Yuan Chiu, Lasse Peters +6
Decision-making in multi-player games can be extremely challenging, particularly under uncertainty. In this work, we propose a new sample-based approximation to a class of stochast…
Cost Inference for Feedback Dynamic Games from Noisy Partial State Observations and Incomplete Trajectories
Jingqi Li, Chih-Yuan Chiu, Lasse Peters +3
In multi-agent dynamic games, the Nash equilibrium state trajectory of each agent is determined by its cost function and the information pattern of the game. However, the cost and…
Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning
Jingqi Li, Donggun Lee, Somayeh Sojoudi +1
In this paper, we consider the infinite-horizon reach-avoid zero-sum game problem, where the goal is to find a set in the state space, referred to as the reach-avoid set, such that…
Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification
Brendon G. Anderson, Ziye Ma, Jingqi Li +1
In this paper, we study certifying the robustness of ReLU neural networks against adversarial input perturbations. To diminish the relaxation error suffered by the popular linear p…
Tightened Convex Relaxations for Neural Network Robustness Certification
Brendon G. Anderson, Ziye Ma, Jingqi Li +1
In this paper, we consider the problem of certifying the robustness of neural networks to perturbed and adversarial input data. Such certification is imperative for the application…