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Nathaniel Wong
Nathaniel Wong
Google DeepMind
Verified email at google.com
Title
Cited by
Cited by
Year
Open-ended learning leads to generally capable agents
OEL Team, A Stooke, A Mahajan, C Barros, C Deck, J Bauer, J Sygnowski, ...
arXiv preprint arXiv:2107.12808, 2021
1592021
Human instruction-following with deep reinforcement learning via transfer-learning from text
F Hill, S Mokra, N Wong, T Harley
arXiv preprint arXiv:2005.09382, 2020
892020
Grounded language learning fast and slow
F Hill, O Tieleman, T Von Glehn, N Wong, H Merzic, S Clark
arXiv preprint arXiv:2009.01719, 2020
792020
Imitating interactive intelligence
J Abramson, A Ahuja, I Barr, A Brussee, F Carnevale, M Cassin, ...
arXiv preprint arXiv:2012.05672, 2020
702020
Creating multimodal interactive agents with imitation and self-supervised learning
DMIA Team, J Abramson, A Ahuja, A Brussee, F Carnevale, M Cassin, ...
arXiv preprint arXiv:2112.03763, 2021
442021
Improving multimodal interactive agents with reinforcement learning from human feedback
J Abramson, A Ahuja, F Carnevale, P Georgiev, A Goldin, A Hung, ...
arXiv preprint arXiv:2211.11602, 2022
272022
Scaling Instructable Agents Across Many Simulated Worlds
MA Raad, A Ahuja, C Barros, F Besse, A Bolt, A Bolton, B Brownfield, ...
arXiv preprint arXiv:2404.10179, 2024
72024
Open-ended learning leads to generally capable agents (2021)
OEL Team, A Stooke, A Mahajan, C Barros, C Deck, J Bauer, J Sygnowski, ...
URL https://arxiv. org/abs/2107.12808, 0
6
Scaling instructable agents across many simulated worlds
M Abi Raad, A Ahuja, C Barros, F Besse, A Bolt, A Bolton, B Brownfield, ...
arXiv e-prints, arXiv: 2404.10179, 2024
42024
Evaluating multimodal interactive agents
J Abramson, A Ahuja, F Carnevale, P Georgiev, A Goldin, A Hung, ...
arXiv preprint arXiv:2205.13274, 2022
32022
Robust Instruction-Following in a Situated Agent via Transfer-Learning from Text
F Hill, S Mokra, N Wong, T Harley
12019
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Articles 1–11