Towards Graph Representation Learning in Emergent Communication
Abstract
Recent findings in neuroscience suggest that the human brain represents information in a geometric structure (for instance, through conceptual spaces). In order to communicate, we flatten the complex representation of entities and their attributes into a single word or a sentence. In this paper we use graph convolutional networks to support the evolution of language and cooperation in multi-agent systems. Motivated by an image-based referential game, we propose a graph referential game with varying degrees of complexity, and we provide strong baseline models that exhibit desirable properties in terms of language emergence and cooperation. We show that the emerged communication protocol is robust, that the agents uncover the true factors of variation in the game, and that they learn to generalize beyond the samples encountered during training.
Cite
@article{arxiv.2001.09063,
title = {Towards Graph Representation Learning in Emergent Communication},
author = {Agnieszka Słowik and Abhinav Gupta and William L. Hamilton and Mateja Jamnik and Sean B. Holden},
journal= {arXiv preprint arXiv:2001.09063},
year = {2020}
}
Comments
The first two authors contributed equally. Accepted at the Reinforcement Learning in Games workshop at AAAI 2020