Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia

1 code implementation • 6 Dec 2023 • Alexander Sasha Vezhnevets, John P. Agapiou, Avia Aharon, Ron Ziv, Jayd Matyas, Edgar A. Duéñez-Guzmán, William A. Cunningham, Simon Osindero, Danny Karmon, Joel Z. Leibo

Agent-based modeling has been around for decades, and applied widely across the social and natural sciences.

Common Sense Reasoning

Melting Pot 2.0

2 code implementations • 24 Nov 2022 • John P. Agapiou, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán, Jayd Matyas, Yiran Mao, Peter Sunehag, Raphael Köster, Udari Madhushani, Kavya Kopparapu, Ramona Comanescu, DJ Strouse, Michael B. Johanson, Sukhdeep Singh, Julia Haas, Igor Mordatch, Dean Mobbs, Joel Z. Leibo

Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios.

Artificial Life Navigate

Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria

no code implementations • 5 Jan 2022 • Kavya Kopparapu, Edgar A. Duéñez-Guzmán, Jayd Matyas, Alexander Sasha Vezhnevets, John P. Agapiou, Kevin R. McKee, Richard Everett, Janusz Marecki, Joel Z. Leibo, Thore Graepel

A key challenge in the study of multiagent cooperation is the need for individual agents not only to cooperate effectively, but to decide with whom to cooperate.

Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot

no code implementations • 14 Jul 2021 • Joel Z. Leibo, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charles Beattie, Igor Mordatch, Thore Graepel

Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess generalization to novel situations as their primary objective (unlike supervised-learning benchmarks).

Multi-agent Reinforcement Learning reinforcement-learning +1