1 citations · 1 across the 4 of their papers we have counts for
7 papers
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…
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…
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…
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…