Learning to request guidance in emergent language
Benjamin Kolb, Leon Lang, Henning Bartsch, Arwin Gansekoele, Raymond Koopmanschap, Leonardo Romor, David Speck, Mathijs Mul, Elia Bruni
Abstract
Previous research into agent communication has shown that a pre-trained guide can speed up the learning process of an imitation learning agent. The guide achieves this by providing the agent with discrete messages in an emerged language about how to solve the task. We extend this one-directional communication by a one-bit communication channel from the learner back to the guide: It is able to ask the guide for help, and we limit the guidance by penalizing the learner for these requests. During training, the agent learns to control this gate based on its current observation. We find that the amount of requested guidance decreases over time and guidance is requested in situations of high uncertainty. We investigate the agent’s performance in cases of open and closed gates and discuss potential motives for the observed gating behavior.- Anthology ID:
- D19-6407
- Volume:
- Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Aditya Mogadala, Dietrich Klakow, Sandro Pezzelle, Marie-Francine Moens
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 41–50
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/D19-6407/
- DOI:
- 10.18653/v1/D19-6407
- Cite (ACL):
- Benjamin Kolb, Leon Lang, Henning Bartsch, Arwin Gansekoele, Raymond Koopmanschap, Leonardo Romor, David Speck, Mathijs Mul, and Elia Bruni. 2019. Learning to request guidance in emergent language. In Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN), pages 41–50, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Learning to request guidance in emergent language (Kolb et al., 2019)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/D19-6407.pdf