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
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Journal Article
A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning
TL;DR: A practical algorithm named variational maximum mutual information multi-agent actor-critic (VM3-AC) is proposed, which follows centralized learning with decentralized execution (CTDE) and numerical results show that VM3- AC outperforms MADDPG and other MARL algorithms in multi- agent tasks requiring coordination.
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Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning.
TL;DR: This paper proposes a method based on QMIX that is able to achieve centralized training with decentralized execution for ridesharing and shows that the model performs better than the IDQN baseline on a fixed grid size and is able of generalize well to smaller or larger grid sizes.
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A Regularized Opponent Model with Maximum Entropy Objective
TL;DR: In this article, a regularized opponent model with maximum entropy objective (ROMMEO) was proposed to improve the performance of training agents theoretically and empirically in cooperative games, and a tabular Q-iteration method was introduced to optimize it.
Journal ArticleDOI
GraphComm: Efficient Graph Convolutional Communication for Multiagent Cooperation
TL;DR: A graph convolutional communication method (GraphComm) for multiagent cooperation to relive the bottleneck with a variational information bottleneck is used to encode the observations and intentions compactly to improve the effectiveness and robustness of the multiround communication process.
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Emergent Road Rules In Multi-Agent Driving Environments.
TL;DR: Empirical evidence is provided that suggests that -- instead of hard-coding road rules into self-driving algorithms -- a scalable alternative may be to design multi-agent environments in which road rules emerge as optimal solutions to the problem of maximizing traffic flow.
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