Multi-Agent

Social and Affective Machine Learning
My PhD Thesis spans both Social Reinforcement Learning and Affective Computing, investigating how affective and social intelligence can enhance machine learning algorithms, and how machine learning can enhance our ability to predict and understand human affective and social phenomena.
Natasha Jaques
2019 In Massachusetts Institute of Technology
PDF Cite Thesis Defense CV News write-up
Social and Affective Machine Learning
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
Social influence is a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning, through rewarding agents for having causal influence over other agents’ actions, thus increasing mutual information between agents’ actions. Optimizing for influence leads to agents learning emergent communication protocols. Unlike prior work, influence can be computed in a fully decentralized manner.
Natasha Jaques, A. Lazaridou, E. Hughes, C. Gulcehre, P. A. Ortega, D. J. Strouse, J.Z. Leibo, N. de Freitas
2019 In International Conference on Machine Learning (ICML) Best Paper Honourable Mention (top 0.26% of submissions)
PDF Cite Poster Slides Videos ICML talk IEEE Spectrum article ICML 2019 Best Papers
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning