@inproceedings{kolb-etal-2019-learning,
title = "Learning to request guidance in emergent language",
author = "Kolb, Benjamin and
Lang, Leon and
Bartsch, Henning and
Gansekoele, Arwin and
Koopmanschap, Raymond and
Romor, Leonardo and
Speck, David and
Mul, Mathijs and
Bruni, Elia",
editor = "Mogadala, Aditya and
Klakow, Dietrich and
Pezzelle, Sandro and
Moens, Marie-Francine",
booktitle = "Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6407",
doi = "10.18653/v1/D19-6407",
pages = "41--50",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Learning to request guidance in emergent language
%A Kolb, Benjamin
%A Lang, Leon
%A Bartsch, Henning
%A Gansekoele, Arwin
%A Koopmanschap, Raymond
%A Romor, Leonardo
%A Speck, David
%A Mul, Mathijs
%A Bruni, Elia
%Y Mogadala, Aditya
%Y Klakow, Dietrich
%Y Pezzelle, Sandro
%Y Moens, Marie-Francine
%S Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F kolb-etal-2019-learning
%X 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.
%R 10.18653/v1/D19-6407
%U https://aclanthology.org/D19-6407
%U https://doi.org/10.18653/v1/D19-6407
%P 41-50
Markdown (Informal)
[Learning to request guidance in emergent language](https://aclanthology.org/D19-6407) (Kolb et al., 2019)
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.