153 citations · 343 across the 18 of their papers we have counts for
19 papers
Approximating the Core via Iterative Coalition Sampling
Ian Gemp, Marc Lanctot, Luke Marris +11
The core is a central solution concept in cooperative game theory, defined as the set of feasible allocations or payments such that no subset of agents has incentive to break away…
TacticAI: an AI assistant for football tactics
Zhe Wang, Petar Veličković, Daniel Hennes +20
Identifying key patterns of tactics implemented by rival teams, and developing effective responses, lies at the heart of modern football. However, doing so algorithmically remains…
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.…
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…
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
Ian Gemp, Thomas Anthony, Yoram Bachrach +24
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simul…
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
Julien Perolat, Bart de Vylder, Daniel Hennes +31
We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few…