Collaborating with humans without human data DJ Strouse, K McKee, M Botvinick, E Hughes, R Everett Advances in Neural Information Processing Systems 34, 14502-14515, 2021 | 161 | 2021 |
Learning to play no-press diplomacy with best response policy iteration T Anthony, T Eccles, A Tacchetti, J Kramár, I Gemp, T Hudson, N Porcel, ... Advances in Neural Information Processing Systems 33, 17987-18003, 2020 | 52 | 2020 |
The anatomy of online deception: What makes automated text convincing? RM Everett, JRC Nurse, A Erola Proceedings of the 31st Annual ACM Symposium on Applied Computing, 1115-1120, 2016 | 50 | 2016 |
Bounds and dynamics for empirical game theoretic analysis K Tuyls, J Perolat, M Lanctot, E Hughes, R Everett, JZ Leibo, ... Autonomous Agents and Multi-Agent Systems 34, 1-30, 2020 | 44 | 2020 |
Spurious normativity enhances learning of compliance and enforcement behavior in artificial agents R Köster, D Hadfield-Menell, R Everett, L Weidinger, GK Hadfield, ... Proceedings of the National Academy of Sciences 119 (3), e2106028118, 2022 | 40 | 2022 |
Negotiating team formation using deep reinforcement learning Y Bachrach, R Everett, E Hughes, A Lazaridou, JZ Leibo, M Lanctot, ... Artificial Intelligence 288, 103356, 2020 | 37 | 2020 |
Using the Veil of Ignorance to align AI systems with principles of justice L Weidinger, KR McKee, R Everett, S Huang, TO Zhu, MJ Chadwick, ... Proceedings of the National Academy of Sciences 120 (18), e2213709120, 2023 | 30 | 2023 |
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning KR McKee, JZ Leibo, C Beattie, R Everett Autonomous Agents and Multi-Agent Systems 36 (1), 21, 2022 | 30 | 2022 |
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent I Gemp, R Savani, M Lanctot, Y Bachrach, T Anthony, R Everett, ... arXiv preprint arXiv:2106.01285, 2021 | 18 | 2021 |
D3C: Reducing the Price of Anarchy in Multi-Agent Learning I Gemp, KR McKee, R Everett, EA Duéñez-Guzmán, Y Bachrach, ... arXiv preprint arXiv:2010.00575, 2020 | 16 | 2020 |
Uncovering surprising behaviors in reinforcement learning via worst-case analysis A Ruderman, R Everett, B Sikder, H Soyer, J Uesato, A Kumar, C Beattie, ... | 13 | 2019 |
Scaffolding cooperation in human groups with deep reinforcement learning KR McKee, A Tacchetti, MA Bakker, J Balaguer, L Campbell-Gillingham, ... Nature Human Behaviour 7 (10), 1787-1796, 2023 | 12 | 2023 |
Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria K Kopparapu, EA Duéñez-Guzmán, J Matyas, AS Vezhnevets, ... arXiv preprint arXiv:2201.01816, 2022 | 11 | 2022 |
Quantifying environment and population diversity in multi-agent reinforcement learning KR McKee, JZ Leibo, C Beattie, R Everett arXiv preprint arXiv:2102.08370, 2021 | 11 | 2021 |
Learning few-shot imitation as cultural transmission A Bhoopchand, B Brownfield, A Collister, A Dal Lago, A Edwards, ... Nature Communications 14 (1), 7536, 2023 | 10 | 2023 |
Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas U Madhushani, KR McKee, JP Agapiou, JZ Leibo, R Everett, T Anthony, ... arXiv preprint arXiv:2305.00768, 2023 | 7 | 2023 |
Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents. R Everett, AD Cobb, A Markham, SJ Roberts AAMAS, 1943-1945, 2019 | 7 | 2019 |
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus AD Cobb, R Everett, A Markham, SJ Roberts Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 6 | 2018 |
Learning robust real-time cultural transmission without human data A Bhoopchand, B Brownfield, A Collister, AD Lago, A Edwards, R Everett, ... ArXiv, abs/2203.00715, 2022 | 5 | 2022 |
Inferring agent objectives at different scales of a complex adaptive system D Hendricks, A Cobb, R Everett, J Downing, SJ Roberts arXiv preprint arXiv:1712.01137, 2017 | 5 | 2017 |