Abstract
Several approaches have recently been proposed for learning decentralized deep multiagent policies that coordinate via a differentiable communication channel. While these policies are effective for many tasks, interpretation of their induced communication strategies has remained a challenge. Here we propose to interpret agents’ messages by translating them. Unlike in typical machine translation problems, we have no parallel data to learn from. Instead we develop a translation model based on the insight that agent messages and natural language strings mean the same thing if they induce the same belief about the world in a listener. We present theoretical guarantees and empirical evidence that our approach preserves both the semantics and pragmatics of messages by ensuring that players communicating through a translation layer do not suffer a substantial loss in reward relative to players with a common language.- Anthology ID:
- P17-1022
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 232–242
- Language:
- URL:
- https://aclanthology.org/P17-1022
- DOI:
- 10.18653/v1/P17-1022
- Cite (ACL):
- Jacob Andreas, Anca Dragan, and Dan Klein. 2017. Translating Neuralese. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 232–242, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Translating Neuralese (Andreas et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/P17-1022.pdf
- Code
- jacobandreas/neuralese