Papers, articles, and other material
- “Emergence of Grounded Compositional Language in Multi-Agent Populations” by Igor Mordatch, Pieter Abbeel. Mordatch and Abbeel demonstrate the emergence of a language with relevant, materially grounded noun, adjective, and verb symbols from a simple reinforcement learning multi-agent collaboration task.
- See slides from a JC presentation given on this paper.
- “On the Spontaneous Emergence of Discrete and Compositional Signals” by Nur Geffen Lan, Emmanuel Chemla, Shane Steinert-Threlkeld. Lan et al. demonstrate that a signalling language games can be understood as an autoencoder problem and demonstrate that discrete messages naturally arise within these messages from training autoencoders in such contexts.
- “Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning” by Sheng Li, Yutai Zhou, Ross Allen, Mykel J. Kochenderfer.
- “Compositional Observer Communication Learning from Raw Visual Input” by Edward Choi, Angeliki Lazaridou, Nando de Freitas.
- “An Improvement for Capsule Networks using Depthwise Separable Convolution” by Nguyen Huu Phong, Bernardete Ribeiro.
- Looking into using this as our visual unit for Siamese architecture.
More research and resources forthcoming.