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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Conditional Expectation based Value Decomposition for Scalable On-Demand Ride Pooling
TL;DR: The authors proposed a conditional expectation-based value decomposition (CEVD) approach for on-demand ride pooling, which considers the impact of other agents actions on individual vehicle value.
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Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
TL;DR: The authors compare three different classes of MARL algorithms (independent learning, centralised multi-agent policy gradient, value decomposition) in a diverse range of cooperative multiagent learning tasks, and provide insights regarding the effectiveness of different learning approaches.
Book ChapterDOI
D3PG: Decomposed Deep Deterministic Policy Gradient for Continuous Control
Yinzhao Dong,Chao Yu,Hongwei Ge +2 more
TL;DR: In this paper, a multi-agent extension of DDPG is proposed, which decomposes the global critic into a weighted sum of local critics, each of these critics is modeled as an individual learning agent that governs the decision making of a particular joint of a learning robot.
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Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming.
TL;DR: In this article, the authors focus on the case that the global reward is a sum of local rewards, the joint policy factorizes into agents' marginals, and full state observability.
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