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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Journal ArticleDOI
Adaptive Learning: A New Decentralized Reinforcement Learning Approach for Cooperative Multiagent Systems
TL;DR: Experimental results show that the proposed algorithm can effectively improve the coordination ability of a MAS and the variance of the training results is more stable than that of the hysteretic Q learning(HQL) algorithm.
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AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning
TL;DR: This work proposes a method that can learn sub-group relationships and how they can be combined, ultimately improving knowledge sharing and generalization across scenarios and extends QMIX for dynamic MARL in two ways.
Proceedings ArticleDOI
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning
TL;DR: This paper proposes a novel approach to multi-agent reinforcement learning that utilizes a relevance graph representation of the environment obtained by a self-attention mechanism, and a message-generation technique, and shows that it significantly outperforms state-of-the-art MARL solutions.
Proceedings Article
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
TL;DR: In this paper, the authors proposed a shared experience actor-critic (SEAC) algorithm, which applies experience sharing in an actor critic framework to explore sparse reward multi-agent environments.
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Natural Emergence of Heterogeneous Strategies in Artificially Intelligent Competitive Teams
Ankur Deka,Katia Sycara +1 more
TL;DR: This work develops a competitive multi agent environment called FortAttack in which two teams compete against each other, and corroborates that modeling agents with Graph Neural Networks and training them with Reinforcement Learning leads to the evolution of increasingly complex strategies for each team.
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