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Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.

T. Rashid, M. Samvelyan, C. de Witt, G. Farquhar, J. Foerster, and S. Whiteson. J. Mach. Learn. Res., (2020)

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Mikayel Khanbekyan

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Insights From the NeurIPS 2021 NetHack Challenge.E. Hambro, S. Mohanty, D. Babaev, M. Byeon, D. Chakraborty, E. Grefenstette, M. Jiang, D. Jo, A. Kanervisto, J. Kim and 19 other author(s). NeurIPS (Competition and Demos), volume 176 of Proceedings of Machine Learning Research, page 41-52. PMLR, (2021)MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research.M. Samvelyan, R. Kirk, V. Kurin, J. Parker-Holder, M. Jiang, E. Hambro, F. Petroni, H. Küttler, E. Grefenstette, and T. Rocktäschel. NeurIPS Datasets and Benchmarks, (2021)Multi-Agent Diagnostics for Robustness via Illuminated Diversity.M. Samvelyan, D. Paglieri, M. Jiang, J. Parker-Holder, and T. Rocktäschel. AAMAS, page 1630-1644. International Foundation for Autonomous Agents and Multiagent Systems / ACM, (2024)JaxMARL: Multi-Agent RL Environments in JAX.A. Rutherford, B. Ellis, M. Gallici, J. Cook, A. Lupu, G. Ingvarsson, T. Willi, A. Khan, C. de Witt, A. Souly and 10 other author(s). CoRR, (2023)Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning.A. Mahajan, M. Samvelyan, L. Mao, V. Makoviychuk, A. Garg, J. Kossaifi, S. Whiteson, Y. Zhu, and A. Anandkumar. ICML, volume 139 of Proceedings of Machine Learning Research, page 7301-7312. PMLR, (2021)JaxMARL: Multi-Agent RL Environments and Algorithms in JAX.A. Rutherford, B. Ellis, M. Gallici, J. Cook, A. Lupu, G. Ingvarsson, T. Willi, A. Khan, C. de Witt, A. Souly and 10 other author(s). AAMAS, page 2444-2446. International Foundation for Autonomous Agents and Multiagent Systems / ACM, (2024)Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.T. Rashid, M. Samvelyan, C. de Witt, G. Farquhar, J. Foerster, and S. Whiteson. J. Mach. Learn. Res., (2020)GriddlyJS: A Web IDE for Reinforcement Learning.C. Bamford, M. Jiang, M. Samvelyan, and T. Rocktäschel. NeurIPS, (2022)QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.T. Rashid, M. Samvelyan, C. de Witt, G. Farquhar, J. Foerster, and S. Whiteson. ICML, volume 80 of Proceedings of Machine Learning Research, page 4292-4301. PMLR, (2018)Evolving Curricula with Regret-Based Environment Design.J. Parker-Holder, M. Jiang, M. Dennis, M. Samvelyan, J. Foerster, E. Grefenstette, and T. Rocktäschel. ICML, volume 162 of Proceedings of Machine Learning Research, page 17473-17498. PMLR, (2022)

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Disambiguation of "Samvelyan, Mikayel"

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Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.

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Mikayel Khanbekyan

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Disambiguation of "Samvelyan, Mikayel"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.

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Mikayel Khanbekyan

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Chicago
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Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.