3 citations · 6 across the 4 of their papers we have counts for
4 papers · 1 filter
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…
Modelling Cooperation in Network Games with Spatio-Temporal Complexity
Michiel A. Bakker, Richard Everett, Laura Weidinger +4
The real world is awash with multi-agent problems that require collective action by self-interested agents, from the routing of packets across a computer network to the management…
Model-free conventions in multi-agent reinforcement learning with heterogeneous preferences
Raphael Köster, Kevin R. McKee, Richard Everett +7
Game theoretic views of convention generally rest on notions of common knowledge and hyper-rational models of individual behavior. However, decades of work in behavioral economics…
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus
Adam D. Cobb, Richard Everett, Andrew Markham +1
In systems of multiple agents, identifying the cause of observed agent dynamics is challenging. Often, these agents operate in diverse, non-stationary environments, where models re…