15 citations · 22 across the 5 of their papers we have counts for
5 papers
Co-Learning Empirical Games and World Models
Max Olan Smith, Michael P. Wellman
Game-based decision-making involves reasoning over both world dynamics and strategic interactions among the agents. Typically, empirical models capturing these respective aspects a…
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.…
Iterative Empirical Game Solving via Single Policy Best Response
Max Olan Smith, Thomas Anthony, Michael P. Wellman
Policy-Space Response Oracles (PSRO) is a general algorithmic framework for learning policies in multiagent systems by interleaving empirical game analysis with deep reinforcement…
Learning to Play against Any Mixture of Opponents
Max Olan Smith, Thomas Anthony, Yongzhao Wang +1
Intuitively, experience playing against one mixture of opponents in a given domain should be relevant for a different mixture in the same domain. We propose a transfer learning met…
No Press Diplomacy: Modeling Multi-Agent Gameplay
Philip Paquette, Yuchen Lu, Steven Bocco +6
Diplomacy is a seven-player non-stochastic, non-cooperative game, where agents acquire resources through a mix of teamwork and betrayal. Reliance on trust and coordination makes Di…