On the interaction between supervision and self-play in emergent communication

Ryan Lowe, Abhinav Gupta, Jakob Foerster, Douwe Kiela, Joelle Pineau

Keywords: nlp, sample efficiency

Abstract: A promising approach for teaching artificial agents to use natural language involves using human-in-the-loop training. However, recent work suggests that current machine learning methods are too data inefficient to be trained in this way from scratch. In this paper, we investigate the relationship between two categories of learning signals with the ultimate goal of improving sample efficiency: imitating human language data via supervised learning, and maximizing reward in a simulated multi-agent environment via self-play (as done in emergent communication), and introduce the term supervised self-play (S2P) for algorithms using both of these signals. We find that first training agents via supervised learning on human data followed by self-play outperforms the converse, suggesting that it is not beneficial to emerge languages from scratch. We then empirically investigate various S2P schedules that begin with supervised learning in two environments: a Lewis signaling game with symbolic inputs, and an image-based referential game with natural language descriptions. Lastly, we introduce population based approaches to S2P, which further improves the performance over single-agent methods.

Similar Papers

Emergent Tool Use From Multi-Agent Autocurricula
Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch,
Semantically-Guided Representation Learning for Self-Supervised Monocular Depth
Vitor Guizilini, Rui Hou, Jie Li, Rares Ambrus, Adrien Gaidon,
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning
Qian Long, Zihan Zhou, Abhinav Gupta, Fei Fang, Yi Wu†, Xiaolong Wang†,