1.2k citations · 4.4k across the 42 of their papers we have counts for
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Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
David Silver, Thomas Hubert, Julian Schrittwieser +10
The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques,…
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys +5
To achieve general intelligence, agents must learn how to interact with others in a shared environment: this is the challenge of multiagent reinforcement learning (MARL). The simpl…
Symmetric Decomposition of Asymmetric Games
Karl Tuyls, Julien Perolat, Marc Lanctot +6
We introduce new theoretical insights into two-population asymmetric games allowing for an elegant symmetric decomposition into two single population symmetric games. Specifically,…
A multi-agent reinforcement learning model of common-pool resource appropriation
Julien Perolat, Joel Z. Leibo, Vinicius Zambaldi +3
Humanity faces numerous problems of common-pool resource appropriation. This class of multi-agent social dilemma includes the problems of ensuring sustainable use of fresh water, c…
Value-Decomposition Networks For Cooperative Multi-Agent Learning
Peter Sunehag, Guy Lever, Audrunas Gruslys +8
We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal. This class of learning problems is difficult because of the often large co…
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
Joel Z. Leibo, Vinicius Zambaldi, Marc Lanctot +2
Matrix games like Prisoner's Dilemma have guided research on social dilemmas for decades. However, they necessarily treat the choice to cooperate or defect as an atomic action. In…