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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Proceedings ArticleDOI
Distributed Heuristic Multi-Agent Path Finding with Communication
Ziyuan Ma,Yudong Luo,Hang Ma +2 more
TL;DR: In this paper, the authors combine communication with deep Q-learning to provide a novel learning based method for multi-agent path finding, where agents achieve cooperation via graph convolution and embed the potential choices of shortest paths from single source as heuristic guidance instead of using a specific path as in most existing works.
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Deep reinforcement learning for large-scale epidemic control
Pieter Libin,Arno Moonens,Timothy Verstraeten,Fabian Perez-Sanjines,Niel Hens,Philippe Lemey,Ann Nowé +6 more
TL;DR: This experiment shows that deep reinforcement learning can be used to learn mitigation policies in complex epidemiological models with a large state space, and demonstrates that there can be an advantage to consider collaboration between districts when designing prevention strategies.
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Parameter Sharing is Surprisingly Useful for Multi-Agent Deep Reinforcement Learning.
TL;DR: The MAILP model is used to show that increasing training centralization arbitrarily mitigates the slowing of convergence due to nonstationarity, and a formal proof of a set of methods that allow parameter sharing to serve in environments with heterogeneous agents is offered.
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Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms.
TL;DR: This work evaluates and compares three different classes of MARL algorithms in a diverse range of multi-agent learning tasks and shows that algorithm performance depends strongly on environment properties and no algorithm learns efficiently across all learning tasks.
Journal ArticleDOI
Multiagent Adversarial Collaborative Learning via Mean-Field Theory
TL;DR: This work proposes an adversarial collaborative learning method in a mixed cooperative–competitive environment, exploiting friend-or-foe Q-learning and mean-field theory, and simplifies the interactions as those between a single agent and the mean effects of friends and opponents.
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