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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Hierarchical Reinforcement Learning for Multi-agent MOBA Game.
Zhijian Zhang,Haozheng Li,Luo Zhang,Tianyin Zheng,Ting Zhang,Xiong Hao,Xiaoxin Chen,Min Chen,Fangxu Xiao,Wei Zhou +9 more
TL;DR: A novel hierarchical reinforcement learning model for mastering Multiplayer Online Battle Arena (MOBA) games, a sub-genre of RTS games, and designing a dense reward function for multi-agent cooperation in the absence of game engine or Application Programming Interface (API).
Book ChapterDOI
Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination
TL;DR: In this paper, the authors propose a dynamic termination Bellman equation that allows the agents to flexibly terminate their options in order to balance flexibility and predictability in a multi-agent pursuit and taxi task.
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Learning to Play against Any Mixture of Opponents.
TL;DR: Q-Mixing augmented with the opponent classifier function performs comparably, and with lower variance, than training directly against a mixed-strategy opponent, and is able to successfully transfer knowledge across any mixture of opponents.
Journal Article
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning
TL;DR: This work proposes a novel framework for value function factorization in multi-agent deep reinforcement learning using graph neural networks (GNNs), which considers the team of agents as the set of nodes of a complete directed graph, whose edge weights are governed by an attention mechanism.
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Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem
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