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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Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks
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Playtesting in Match 3 Game Using Strategic Plays via Reinforcement Learning
TL;DR: It is demonstrated that it is possible for the level designer to measure the difficulty of the level via playtesting various missions via reinforcement learning and level testing standards for several types of missions in Match 3 games are provided.
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BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)
Marek Rosa,Olga Afanasjeva,Simon Andersson,Joseph Davidson,Nicholas Guttenberg,Petr Hlubuček,Martin Poliak,Jaroslav Vitku,Jan Feyereisl +8 more
TL;DR: A novel memory-based multi-agent meta-learning architecture and learning procedure that allows for learning of a shared communication policy that enables the emergence of rapid adaptation to new and unseen environments by learning to learn learning algorithms through communication.
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IADRL: Imitation Augmented Deep Reinforcement Learning Enabled UGV-UAV Coalition for Tasking in Complex Environments
TL;DR: An Imitation Augmented Deep Reinforcement Learning (IADRL) model is proposed that enables a UGV and UAV to form a coalition that is complementary and cooperative for completing tasks that they are incapable of achieving alone.
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Scalable Reinforcement Learning Policies for Multi-Agent Control.
TL;DR: A masking heuristic is developed that allows training on smaller problems with few pursuers-targets and execution on much larger problems and is discussed how it enables a hedging behavior between pursuers that leads to a weak form of cooperation in spite of completely decentralized control execution.
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