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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Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication.
TL;DR: In this article, a graph attention exchange network (GAXNet) is proposed for air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user.
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Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading.
TL;DR: New RL framework based on hybrid algorithm which leverages between supervised learning and RL algorithm and uses meaningful observations such order book and settlement data from experience watching scalpers trading, which could make agent mimic scalpers as much as possible.
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MARL with General Utilities via Decentralized Shadow Reward Actor-Critic.
TL;DR: In this article, the authors propose a new mechanism for cooperation in multi-agent reinforcement learning (MARL) based upon any nonlinear function of the team's long-term state-action occupancy measure, i.e., a general utility.
Proceedings ArticleDOI
QMIX Algorithm for Coordinated Welding of Multiple Robots
Liangchuang Liao,Yang-Yang Chen +1 more
TL;DR: In this article, the coordinated welding control based on deep multi-agent reinforcement learning (QMIX) is considered, and a novel reward composed of trajectory optimization, collision avoidance and the task done is designed, which is proved by the simulation in the grid-world environment.
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Quasi-Equivalence Discovery for Zero-Shot Emergent Communication.
TL;DR: In this article, the Quasi-Equivalence Discovery (QED) algorithm is used to discover protocols that can generalize to independently trained agents in a zero-shot coordination (ZSC) setting.
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