Minqi Jiang
Minqi Jiang
University College London & Meta AI Research
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
Prioritized level replay
M Jiang, E Grefenstette, T Rocktäschel
International Conference on Machine Learning, 4940-4950, 2021
Motion responsive user interface for realtime language translation
AJ Cuthbert, JJ Estelle, MR Hughes, S Goyal, MS Jiang
US Patent 9,355,094, 2016
Minihack the planet: A sandbox for open-ended reinforcement learning research
M Samvelyan, R Kirk, V Kurin, J Parker-Holder, M Jiang, E Hambro, ...
arXiv preprint arXiv:2109.13202, 2021
WordCraft: An Environment for Benchmarking Commonsense Agents
M Jiang, J Luketina, N Nardelli, P Minervini, PHS Torr, S Whiteson, ...
Language in Reinforcement Learning Workshop at ICML 2020, 2020
Replay-Guided Adversarial Environment Design
M Jiang*, M Dennis*, J Parker-Holder, J Foerster, E Grefenstette, ...
NeurIPS 2021, 2021
Improving intrinsic exploration with language abstractions
J Mu, V Zhong, R Raileanu, M Jiang, N Goodman, T Rocktäschel, ...
arXiv preprint arXiv:2202.08938, 2022
Insights From the NeurIPS 2021 NetHack Challenge
E Hambro, S Mohanty, D Babaev, M Byeon, D Chakraborty, ...
arXiv preprint arXiv:2203.11889, 2022
Evolving Curricula with Regret-Based Environment Design
J Parker-Holder*, M Jiang*, M Dennis, M Samvelyan, J Foerster, ...
ICML 2022,, 2022
Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning
Z Jiang, P Minervini, M Jiang, T Rocktäschel
AAMAS 2021 (Oral), 2021
Resolving Causal Confusion in Reinforcement Learning via Robust Exploration
C Lyle, A Zhang, M Jiang, J Pineau, Y Gal
Self-Supervision for Reinforcement Learning Workshop-ICLR 2021, 2021
A Study of Off-Policy Learning in Environments with Procedural Content Generation
A Ehrenberg, R Kirk, M Jiang, E Grefenstette, T Rocktäschel
ICLR Workshop on Agent Learning in Open-Endedness, 2022
Grounding Aleatoric Uncertainty in Unsupervised Environment Design
M Jiang, MD Dennis, J Parker-Holder, A Lupu, H Kuttler, E Grefenstette, ...
Deep RL Workshop NeurIPS 2021, 2021
Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay
I Korshunova, M Jiang, J Parker-Holder, T Rocktäschel, E Grefenstette
I (Still) Can't Believe It's Not Better! NeurIPS 2021 Workshop, 2021
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