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

, , , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 4292-4301. PMLR, (2018)

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The StarCraft Multi-Agent Challenge., , , , , , , , , and . AAMAS, page 2186-2188. International Foundation for Autonomous Agents and Multiagent Systems, (2019)QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning., , , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 4292-4301. PMLR, (2018)Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning., , , , , and . CoRR, (2020)Estimating α-Rank by Maximizing Information Gain., , and . AAAI, page 5673-5681. AAAI Press, (2021)A New Take on Detecting Insider Threats: Exploring the Use of Hidden Markov Models., , and . MIST@CCS, page 47-56. ACM, (2016)Imitating Human Behaviour with Diffusion Models., , , , , , , , , and 1 other author(s). ICLR, OpenReview.net, (2023)Regularized Softmax Deep Multi-Agent Q-Learning., , , , and . NeurIPS, page 1365-1377. (2021)MAVEN: Multi-Agent Variational Exploration., , , and . NeurIPS, page 7611-7622. (2019)FACMAC: Factored Multi-Agent Centralised Policy Gradients., , , , , , and . NeurIPS, page 12208-12221. (2021)Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning., , , and . NeurIPS, (2020)