| Social influence as intrinsic motivation for multi-agent deep reinforcement learning N Jaques, A Lazaridou, E Hughes, C Gulcehre, P Ortega, DJ Strouse, ... International conference on machine learning, 3040-3049, 2019 | 345* | 2019 |
| The hanabi challenge: A new frontier for ai research N Bard, JN Foerster, S Chandar, N Burch, M Lanctot, HF Song, E Parisotto, ... Artificial Intelligence 280, 103216, 2020 | 260 | 2020 |
| Inequity aversion improves cooperation in intertemporal social dilemmas E Hughes, JZ Leibo, M Phillips, K Tuyls, E Dueñez-Guzman, ... Advances in neural information processing systems 31, 2018 | 162 | 2018 |
| OpenSpiel: A framework for reinforcement learning in games M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ... arXiv preprint arXiv:1908.09453, 2019 | 157 | 2019 |
| Learning to follow language instructions with adversarial reward induction D Bahdanau, F Hill, J Leike, E Hughes, P Kohli, E Grefenstette arXiv preprint arXiv:1806.01946, 6-9, 2018 | 147* | 2018 |
| Bayesian action decoder for deep multi-agent reinforcement learning J Foerster, F Song, E Hughes, N Burch, I Dunning, S Whiteson, ... International Conference on Machine Learning, 1942-1951, 2019 | 137 | 2019 |
| Open problems in cooperative AI A Dafoe, E Hughes, Y Bachrach, T Collins, KR McKee, JZ Leibo, K Larson, ... arXiv preprint arXiv:2012.08630, 2020 | 117 | 2020 |
| Causal reasoning from meta-reinforcement learning I Dasgupta, J Wang, S Chiappa, J Mitrovic, P Ortega, D Raposo, ... arXiv preprint arXiv:1901.08162, 2019 | 98 | 2019 |
| Autocurricula and the emergence of innovation from social interaction: A manifesto for multi-agent intelligence research JZ Leibo, E Hughes, M Lanctot, T Graepel arXiv preprint arXiv:1903.00742, 2019 | 80 | 2019 |
| Evolving intrinsic motivations for altruistic behavior JX Wang, E Hughes, C Fernando, WM Czarnecki, EA Duéñez-Guzmán, ... arXiv preprint arXiv:1811.05931, 2018 | 62 | 2018 |
| A generalized training approach for multiagent learning P Muller, S Omidshafiei, M Rowland, K Tuyls, J Perolat, S Liu, D Hennes, ... arXiv preprint arXiv:1909.12823, 2019 | 60 | 2019 |
| 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 | 53 | 2021 |
| Social diversity and social preferences in mixed-motive reinforcement learning KR McKee, I Gemp, B McWilliams, EA Duéñez-Guzmán, E Hughes, ... arXiv preprint arXiv:2002.02325, 2020 | 47 | 2020 |
| Learning reciprocity in complex sequential social dilemmas T Eccles, E Hughes, J Kramár, S Wheelwright, JZ Leibo AAMAS, 1934-1936, 2019 | 41* | 2019 |
| Learning to incentivize other learning agents J Yang, A Li, M Farajtabar, P Sunehag, E Hughes, H Zha Advances in Neural Information Processing Systems 33, 15208-15219, 2020 | 34 | 2020 |
| Malthusian reinforcement learning JZ Leibo, J Perolat, E Hughes, S Wheelwright, AH Marblestone, ... arXiv preprint arXiv:1812.07019, 2018 | 32 | 2018 |
| The connected prescription for form factors in twistor space A Brandhuber, E Hughes, R Panerai, B Spence, G Travaglini Journal of High Energy Physics 2016 (11), 1-17, 2016 | 29 | 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 | 25 | 2020 |
| Reinforcement learning agents acquire flocking and symbiotic behaviour in simulated ecosystems P Sunehag, G Lever, S Liu, J Merel, N Heess, JZ Leibo, E Hughes, ... ALIFE 2019: The 2019 Conference on Artificial Life, 103-110, 2019 | 22 | 2019 |
| Negotiating team formation using deep reinforcement learning Y Bachrach, R Everett, E Hughes, A Lazaridou, JZ Leibo, M Lanctot, ... Artificial Intelligence 288, 103356, 2020 | 17 | 2020 |
Joel Z LeiboResearch scientistEmail verificata su google.com
