# BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)

arXiv: https://arxiv.org/abs/1912.01513 · HTML: https://mlsanity.com/paper/1912.01513/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [Eliminating Meta Optimization Through Self-Referential Meta Learning](https://mlsanity.com/paper/2212.14392/) (2022)
- [A Simple Guard for Learned Optimizers](https://mlsanity.com/paper/2201.12426/) (2022)
- [Learning to Optimize with Dynamic Mode Decomposition](https://mlsanity.com/paper/2211.16268/) (2022)
- [Bootstrapping of memetic from genetic evolution via inter-agent selection pressures](https://mlsanity.com/paper/2104.03404/) (2021)
- [The Role of Bio-Inspired Modularity in General Learning](https://mlsanity.com/paper/2109.15097/) (2021)

## Abstract (arXiv)

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'.