Charlie Beattie
Charlie Beattie
Software Engineer, DeepMind
Verified email at
Cited by
Cited by
Human-level control through deep reinforcement learning
V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ...
nature 518 (7540), 529-533, 2015
Human-level performance in 3D multiplayer games with population-based reinforcement learning
M Jaderberg, WM Czarnecki, I Dunning, L Marris, G Lever, AG Castaneda, ...
Science 364 (6443), 859-865, 2019
Vector-based navigation using grid-like representations in artificial agents
A Banino, C Barry, B Uria, C Blundell, T Lillicrap, P Mirowski, A Pritzel, ...
Nature 557 (7705), 429-433, 2018
Massively parallel methods for deep reinforcement learning
A Nair, P Srinivasan, S Blackwell, C Alcicek, R Fearon, A De Maria, ...
arXiv preprint arXiv:1507.04296, 2015
Deepmind lab
C Beattie, JZ Leibo, D Teplyashin, T Ward, M Wainwright, H Küttler, ...
arXiv preprint arXiv:1612.03801, 2016
A multi-agent reinforcement learning model of common-pool resource appropriation
J Perolat, JZ Leibo, V Zambaldi, C Beattie, K Tuyls, T Graepel
Advances in neural information processing systems 30, 2017
Psychlab: a psychology laboratory for deep reinforcement learning agents
JZ Leibo, CM d'Autume, D Zoran, D Amos, C Beattie, K Anderson, ...
arXiv preprint arXiv:1801.08116, 2018
Scalable evaluation of multi-agent reinforcement learning with melting pot
JZ Leibo, EA Dueñez-Guzman, A Vezhnevets, JP Agapiou, P Sunehag, ...
International conference on machine learning, 6187-6199, 2021
Inferring a continuous distribution of atom coordinates from cryo-EM images using VAEs
D Rosenbaum, M Garnelo, M Zielinski, C Beattie, E Clancy, A Huber, ...
arXiv preprint arXiv:2106.14108, 2021
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
Deepmind lab2d
C Beattie, T Köppe, EA Duéñez-Guzmán, JZ Leibo
arXiv preprint arXiv:2011.07027, 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, ...
Deep reinforcement learning models the emergent dynamics of human cooperation
KR McKee, E Hughes, TO Zhu, MJ Chadwick, R Koster, AG Castaneda, ...
arXiv preprint arXiv:2103.04982, 2021
Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis.(2018)
A Ruderman, R Everett, B Sikder, H Soyer, J Uesato, A Kumar, C Beattie, ...
URL https://openreview. net/forum, 2018
Vector-based Navigation using Grid-like Representations in Artificial Agents.
A Pritzel, A Banino, B Uria, BC Zhang, C Barry, C Blundell, C Beattie, ...
Appendix for: Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
JZ Leibo, E Duénez-Guzmán, AS Vezhnevets, JP Agapiou, P Sunehag, ...
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