Open AccessProceedings Article
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid,Mikayel Samvelyan,Christian Schroeder,Gregory Farquhar,Jakob Foerster,Shimon Whiteson +5 more
- pp 4292-4301
TLDR
QMIX employs a network that estimates joint action-values as a complex non-linear combination of per-agent values that condition only on local observations, and structurally enforce that the joint-action value is monotonic in the per- agent values, which allows tractable maximisation of the jointaction-value in off-policy learning.About:
This article is published in International Conference on Machine Learning.The article was published on 2018-07-03 and is currently open access. It has received 505 citations till now. The article focuses on the topics: Reinforcement learning & Monotonic function.read more
Citations
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Multiagent Deep Reinforcement Learning: Challenges and Directions Towards Human-Like Approaches.
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Mava: a research framework for distributed multi-agent reinforcement learning.
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Signal Instructed Coordination in Team Competition.
Liheng Chen,Hongyi Guo,Haifeng Zhang,Fei Fang,Yaoming Zhu,Ming Zhou,Weinan Zhang,Qing Wang,Yong Yu +8 more
TL;DR: Signal Instructed Coordination (SIC), a novel coordination module that can be integrated with most existing models of multi-agent reinforcement learning, casts a common signal sampled from a pre-defined distribution to team members, and adopts an information-theoretic regularization to encourage agents to exploit in learning the instruction of centralized signals.
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