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

Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition

no code implementations • 2 Feb 2023 • Peter Sunehag, Alexander Sasha Vezhnevets, Edgar Duéñez-Guzmán, Igor Mordach, Joel Z. Leibo

The algorithm we propose consists of two parts: an agent architecture and a learning rule.

Q-Learning reinforcement-learning +1

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.

Statistical discrimination in learning agents

no code implementations • 21 Oct 2021 • Edgar A. Duéñez-Guzmán, Kevin R. McKee, Yiran Mao, Ben Coppin, Silvia Chiappa, Alexander Sasha Vezhnevets, Michiel A. Bakker, Yoram Bachrach, Suzanne Sadedin, William Isaac, Karl Tuyls, Joel Z. Leibo

Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics.

Decision Making Multi-agent Reinforcement Learning

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

Options as responses: Grounding behavioural hierarchies in multi-agent RL

no code implementations • 4 Jun 2019 • Alexander Sasha Vezhnevets, Yuhuai Wu, Remi Leblond, Joel Z. Leibo

This paper investigates generalisation in multi-agent games, where the generality of the agent can be evaluated by playing against opponents it hasn't seen during training.

Multi-agent Reinforcement Learning Reinforcement Learning (RL)

StarCraft II: A New Challenge for Reinforcement Learning

11 code implementations • 16 Aug 2017 • Oriol Vinyals, Timo Ewalds, Sergey Bartunov, Petko Georgiev, Alexander Sasha Vezhnevets, Michelle Yeo, Alireza Makhzani, Heinrich Küttler, John Agapiou, Julian Schrittwieser, John Quan, Stephen Gaffney, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, Rodney Tsing

Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain.

reinforcement-learning Reinforcement Learning (RL) +2

FeUdal Networks for Hierarchical Reinforcement Learning

1 code implementation • ICML 2017 • Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu

We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning.

Hierarchical Reinforcement Learning reinforcement-learning +1