7 citations · 10 across the 10 of their papers we have counts for
10 papers
Code-Space Response Oracles: Generating Interpretable Multi-Agent Policies with Large Language Models
Daniel Hennes, Zun Li, John Schultz +1
Recent advances in multi-agent reinforcement learning, particularly Policy-Space Response Oracles (PSRO), have enabled the computation of approximate game-theoretic equilibria in i…
A New Lower Bound for the Random Offerer Mechanism in Bilateral Trade using AI-Guided Evolutionary Search
Yang Cai, Vineet Gupta, Zun Li +1
The celebrated Myerson--Satterthwaite theorem shows that in bilateral trade, no mechanism can be simultaneously fully efficient, Bayesian incentive compatible (BIC), and budget bal…
Discovering Multiagent Learning Algorithms with Large Language Models
Zun Li, John Schultz, Daniel Hennes +1
Much of the advancement in Multi-Agent Reinforcement Learning (MARL) for imperfect-information games has historically depended on the manual, iterative refinement of algorithmic ba…
Ostrakon-VL: Towards Domain-Expert MLLM for Food-Service and Retail Stores
Zhiyong Shen, Gongpeng Zhao, Jun Zhou +10
Multimodal Large Language Models (MLLMs) have recently achieved substantial progress in general-purpose perception and reasoning. Nevertheless, their deployment in Food-Service and…
Code World Models for General Game Playing
Wolfgang Lehrach, Daniel Hennes, Miguel Lazaro-Gredilla +13
Large Language Models (LLMs) reasoning abilities are increasingly being applied to classical board and card games, but the dominant approach -- involving prompting for direct move…
A Meta-Game Evaluation Framework for Deep Multiagent Reinforcement Learning
Zun Li, Michael P. Wellman
Evaluating deep multiagent reinforcement learning (MARL) algorithms is complicated by stochasticity in training and sensitivity of agent performance to the behavior of other agents…
Evaluating Agents using Social Choice Theory
Marc Lanctot, Kate Larson, Yoram Bachrach +6
We argue that many general evaluation problems can be viewed through the lens of voting theory. Each task is interpreted as a separate voter, which requires only ordinal rankings o…
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
Zun Li, Marc Lanctot, Kevin R. McKee +7
Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents' strategies, and exploiting this opponent model by playing a best respo…
Solving Structured Hierarchical Games Using Differential Backward Induction
Zun Li, Feiran Jia, Aditya Mate +4
From large-scale organizations to decentralized political systems, hierarchical strategic decision making is commonplace. We introduce a novel class of structured hierarchical game…
A Game-Theoretic Approach for Hierarchical Epidemic Control
Feiran Jia, Aditya Mate, Zun Li +5
We design and analyze a multi-level game-theoretic model of hierarchical policy interventions for epidemic control, such as those in response to the COVID-19 pandemic. Our model ca…