8 citations · 12 across the 5 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.AI2026
Modular Discovery of General Game-Playing Algorithms with Large Language Models
Zun Li, John Schultz, Marc Lanctot +1
General Game Playing across arbitrary games from rules alone remains challenging due to differing algorithmic requirements across game classes and strict decision-time constraints.…
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…