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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Proceedings ArticleDOI
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning
TL;DR: In this paper, a method that combines distributional reinforcement learning and value decomposition to alleviate the above weaknesses is proposed, which outperforms prior baselines in the StarCraft Multi-Agent Challenge (SMAC) environment.
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Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations
TL;DR: This work shows that under realistic assumptions, a non-uniform distribution of intents and a common-knowledge energy cost, agents that learn to communicate via actuating their joints in a 3D environment can find protocols that generalize to novel partners.
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Hierarchical control of multi-agent reinforcement learning team in real-time strategy (RTS) games
TL;DR: In this article, a hierarchical command and control architecture is proposed, consisting of a single high-level and multiple low-level reinforcement learning agents operating in a dynamic environment, which enables the low level unit agents to make individual decisions while taking commands from the high level commander agent.
Proceedings ArticleDOI
Demand-Side Scheduling Based on Multi-Agent Deep Actor-Critic Learning for Smart Grids
TL;DR: In this article, the authors propose an extension to a multi-agent, deep actor-critic algorithm to address partial observability and the perceived non-stationarity of the environment from the agent's viewpoint.
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CESMA: Centralized Expert Supervises Multi-Agents.
TL;DR: This work considers the reinforcement learning problem of training multiple agents in order to maximize a shared reward, and shows that one can obtain decentralized solutions to a multi-agent problem through imitation learning.
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