8 citations · 12 across the 5 of their papers we have counts for
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cs.GT2026
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…
cs.GT2026
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…
cs.GT2023★ 3 cited
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning
Marc Lanctot, John Schultz, Neil Burch +4
Progress in fields of machine learning and adversarial planning has benefited significantly from benchmark domains, from checkers and the classic UCI data sets to Go and Diplomacy.…