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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Journal ArticleDOI
Emotion Attention-Aware Collaborative Deep Reinforcement Learning for Image Cropping
TL;DR: By modeling image cropping as a decision-making process of reinforcement learning, the model could generate optimal cropping result in a few moving and zooming steps and outperforms state-of-the-art methods on these benchmark datasets.
Posted Content
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning
TL;DR: Zhang et al. as mentioned in this paper proposed neighborhood cognition consistent deep Q-learning and actor-critic to facilitate large-scale multi-agent cooperations, which can be easily combined with existing MARL methods.
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QR-MIX: Distributional Value Function Factorisation for Cooperative Multi-Agent Reinforcement Learning.
TL;DR: The proposed model QR-MIX introduces quantile regression, modeling joint state-action values as a distribution, combining QMIX with Implicit Quantile Network (IQN), and outperforms the previous state-of-the-art method Q MIX in the StarCraft Multi-Agent Challenge (SMAC) environment.
Proceedings ArticleDOI
Hybrid Learning for Multi-agent Cooperation with Sub-optimal Demonstrations
TL;DR: A novel hybrid learning method based on multi-agent actor-critic that outperforms many competing demonstration-based methods and is evaluated on a real-time strategy combat game.
Proceedings Article
RODE: Learning Roles to Decompose Multi-Agent Tasks
TL;DR: In this paper, the authors propose to first decompose joint action spaces into restricted role action spaces by clustering actions according to their effects on the environment and other agents, and then learn a role selector based on action effects.
References
More filters
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Posted Content
Empirical evaluation of gated recurrent neural networks on sequence modeling
TL;DR: These advanced recurrent units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU), are found to be comparable to LSTM.
Deep reinforcement learning with double Q-learning
TL;DR: In this article, the authors show that the DQN algorithm suffers from substantial overestimation in some games in the Atari 2600 domain, and they propose a specific adaptation to the algorithm and show that this algorithm not only reduces the observed overestimations, but also leads to much better performance on several games.
Journal ArticleDOI
Learning from delayed rewards
TL;DR: The invention relates to a circuit for use in a receiver which can receive two-tone/stereo signals which is intended to make a choice between mono or stereo reproduction of signal A or of signal B and vice versa.