In this work, we propose a novel memory-based multi-agent meta-learning architecture and learning procedure that allows for learning of a shared communication policy that enables the emergence of rapid adaptation to new and unseen environments by learning to learn learning algorithms through communication. Behavior, adaptation and learning to adapt emerges from the interactions of homogeneous experts inside a single agent. The proposed architecture should allow for generalization beyond the level seen in existing methods, in part due to the use of a single policy shared by all experts within the agent as well as the inherent modularity of 'Badger'.
@article{arxiv.1912.01513,
title = {BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)},
author = {Marek Rosa and Olga Afanasjeva and Simon Andersson and Joseph Davidson and Nicholas Guttenberg and Petr Hlubuček and Martin Poliak and Jaroslav Vítku and Jan Feyereisl},
journal= {arXiv preprint arXiv:1912.01513},
year = {2019}
}