BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)
This addresses the challenge of meta-learning and adaptation in AI systems, though it appears incremental as it builds on multi-agent and meta-learning concepts.
The paper tackles the problem of enabling rapid adaptation to new environments by proposing a memory-based multi-agent meta-learning architecture where homogeneous experts within a single agent learn a shared communication policy to learn learning algorithms through communication, aiming for improved generalization beyond existing methods.
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'.