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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Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning
TL;DR: A variant of the fitted Q-iteration framework for analyzing multi-agent Q-learning with value decomposition is introduced, and a closed-form solution to the empirical Bellman error minimization with linear value decompositions is derived.
Journal ArticleDOI
Improving multi-target cooperative tracking guidance for UAV swarms using multi-agent reinforcement learning
TL;DR: Wang et al. as mentioned in this paper proposed a decentralized MADRL method using the maximum reciprocal reward to learn cooperative tracking policies for UAV swarms, which reshapes each UAV's reward with a regularization term that is defined as the dot product of the reward vector of all neighbor UAVs and the corresponding dependency vector between the UAV and the neighbors.
Journal ArticleDOI
Multi-agent reinforcement learning with approximate model learning for competitive games.
TL;DR: The method consists of recurrent neural network-based actor-critic networks and deterministic policy gradients that promote cooperation between agents by communication and approximate model learning using auxiliary prediction networks for modeling the state transitions, rewards, and opponent behavior.
Proceedings ArticleDOI
On the Robustness of Cooperative Multi-Agent Reinforcement Learning
TL;DR: In this article, the authors analyze the robustness of cooperative multi-agent reinforcement learning (c-MARL) to adversaries capable of attacking one of the agents on a team.
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M$^3$RL: Mind-aware Multi-agent Management Reinforcement Learning
Tianmin Shu,Yuandong Tian +1 more
TL;DR: In this paper, a Mind-Aware Multi-Agent Management Reinforcement Learning (M^3RL) is proposed to train a super agent to manage workers by inferring their minds based on both current and past observations and then initiating contracts to assign suitable tasks to workers and promise to reward them with corresponding bonuses.
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