49 citations · 108 across the 34 of their papers we have counts for
5 papers · 1 filter
D3C: Reducing the Price of Anarchy in Multi-Agent Learning
Ian Gemp, Kevin R. McKee, Richard Everett +4
In multiagent systems, the complex interaction of fixed incentives can lead agents to outcomes that are poor (inefficient) not only for the group, but also for each individual. Pri…
EigenGame: PCA as a Nash Equilibrium
Ian Gemp, Brian McWilliams, Claire Vernade +1
We present a novel view on principal component analysis (PCA) as a competitive game in which each approximate eigenvector is controlled by a player whose goal is to maximize their…
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Thomas Anthony, Tom Eccles, Andrea Tacchetti +11
Recent advances in deep reinforcement learning (RL) have led to considerable progress in many 2-player zero-sum games, such as Go, Poker and Starcraft. The purely adversarial natur…
Social diversity and social preferences in mixed-motive reinforcement learning
Kevin R. McKee, Ian Gemp, Brian McWilliams +3
Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity. In contrast, studies of reinfor…
Smooth markets: A basic mechanism for organizing gradient-based learners
David Balduzzi, Wojciech M Czarnecki, Thomas W Anthony +5
With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact. Unfortunately, negative results from…