On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning

1 code implementation • 10 Nov 2021 • Andrew Cohen, Ervin Teng, Vincent-Pierre Berges, Ruo-Ping Dong, Hunter Henry, Marwan Mattar, Alexander Zook, Sujoy Ganguly

In this work, we first demonstrate that sample complexity increases with the quantity of absorbing states in a toy supervised learning task for a fully connected network, while attention is more robust to variable size input.

Multi-agent Reinforcement Learning reinforcement-learning +1

AAAI-2019 Workshop on Games and Simulations for Artificial Intelligence

no code implementations • 6 Mar 2019 • Marwan Mattar, Roozbeh Mottaghi, Julian Togelius, Danny Lange

This volume represents the accepted submissions from the AAAI-2019 Workshop on Games and Simulations for Artificial Intelligence held on January 29, 2019 in Honolulu, Hawaii, USA.

Nonparametric Curve Alignment

no code implementations • 2 Feb 2019 • Marwan Mattar, Michael Ross, Erik Learned-Miller

Congealing is a flexible nonparametric data-driven framework for the joint alignment of data.

Clustering

Unity: A General Platform for Intelligent Agents

56 code implementations • 7 Sep 2018 • Arthur Juliani, Vincent-Pierre Berges, Ervin Teng, Andrew Cohen, Jonathan Harper, Chris Elion, Chris Goy, Yuan Gao, Hunter Henry, Marwan Mattar, Danny Lange

Recent advances in artificial intelligence have been driven by the presence of increasingly realistic and complex simulated environments.

Unity

Learning to Align from Scratch

no code implementations • NeurIPS 2012 • Gary Huang, Marwan Mattar, Honglak Lee, Erik G. Learned-Miller

Unsupervised joint alignment of images has been demonstrated to improve performance on recognition tasks such as face verification.

Face Verification