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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UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
TL;DR: A novel MARL approach called UneVEn is presented, which uses universal successor features (USFs) to learn policies of tasks related to the target task, but with simpler reward functions in a sample efficient manner, and significantly outperforms state-of-the-art baselines.
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Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
TL;DR: This work considers the networked multi-agent reinforcement learning (MARL) problem in a fully decentralized setting, and obtains a principled and data-efficient iterative algorithm that is the first MARL algorithm with convergence guarantee in the control, off-policy and non-linear function approximation setting.
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Reward Machines for Cooperative Multi-Agent Reinforcement Learning
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Caching Transient Content for IoT Sensing: Multi-Agent Soft Actor-Critic
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