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Tabish Rashid
Tabish Rashid
Microsoft Research
Bestätigte E-Mail-Adresse bei microsoft.com
Titel
Zitiert von
Zitiert von
Jahr
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
T Rashid, M Samvelyan, CS De Witt, G Farquhar, J Foerster, S Whiteson
Journal of Machine Learning Research 21(178):1−51, 2020, 2020
11862020
The StarCraft Multi-Agent Challenge
M Samvelyan, T Rashid, CS de Witt, G Farquhar, N Nardelli, TGJ Rudner, ...
AAMAS 2019, 2019
4692019
Maven: Multi-agent variational exploration
A Mahajan, T Rashid, M Samvelyan, S Whiteson
Advances in Neural Information Processing Systems, 7613-7624, 2019
2192019
Weighted QMIX: Expanding Monotonic Value Function Factorisation
T Rashid, G Farquhar, B Peng, S Whiteson
Advances in Neural Information Processing Systems 33, 2020, 2020
147*2020
A new take on detecting insider threats: exploring the use of hidden markov models
T Rashid, I Agrafiotis, JRC Nurse
Proceedings of the 8th ACM CCS International Workshop on Managing Insider …, 2016
1402016
FACMAC: Factored Multi-Agent Centralised Policy Gradients
B Peng, T Rashid, CAS de Witt, PA Kamienny, PHS Torr, W Böhmer, ...
arXiv preprint arXiv:2003.06709, 2020
502020
Optimistic Exploration even with a Pessimistic Initialisation
T Rashid, B Peng, W Boehmer, S Whiteson
International Conference on Learning Representations, 2019
332019
Exploration with unreliable intrinsic reward in multi-agent reinforcement learning
W Böhmer, T Rashid, S Whiteson
arXiv preprint arXiv:1906.02138, 2019
222019
Estimating α-Rank by Maximizing Information Gain
T Rashid, C Zhang, K Ciosek
Proceedings of the AAAI Conference on Artificial Intelligence 35 (6), 5673-5681, 2021
52021
Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning
L Pan, T Rashid, B Peng, L Huang, S Whiteson
arXiv preprint arXiv:2103.11883, 2021
42021
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
T Rashid, M Samvelyan, CS de Witt, G Farquhar, J Foerster, S Whiteson
Proceedings of the 35th International Conference on Machine Learning, 2018
2018
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