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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QPLEX: Duplex Dueling Multi-Agent Q-Learning
TL;DR: In this paper, a duplex dueling structure encodes the individual-global-max (IGM) principle into the neural network architecture and thus enables efficient value function learning, which significantly outperforms state-of-the-art baselines.
Journal Article
Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning
TL;DR: It is shown that increasing centralization during learning arbitrarily mitigates the slowing of convergence due to nonstationarity and gives a formal proof of a set of methods that allow parameter sharing to serve in environments with heterogeneous agents.
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Multiple Landmark Detection using Multi-Agent Reinforcement Learning
TL;DR: A new detection approach for multiple landmarks based on multi-agent reinforcement learning based on the hypothesis that the position of all anatomical landmarks is interdependent and non-random within the human anatomy, thus finding one landmark can help to deduce the location of others.
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Weighted QMIX: Expanding Monotonic Value Function Factorisation.
TL;DR: This work proposes two weighting schemes and proves that they recover the correct maximal action for any joint action $Q$-values, and therefore for $Q^*$ as well.
Proceedings Article
Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG
TL;DR: Attention Multi-Agent Deep Deterministic Policy Gradient (ATT-MADDPG) as mentioned in this paper extends DDPG with an attention mechanism to explicitly model the dynamic joint policy of teammates in an adaptive manner.
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