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Sample efficiency and scalability to a large number of agents are two important goals for multi-agent reinforcement learning systems.
Multi-agent reinforcement learning: Independent vs. cooperative agents
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All learning is local: Multi-agent learning in global reward games
Y. han Chang, T. Ho, and L. P. Kaelbling · 2003
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Cooperative multi-agent learning: The state of the art
L. Panait and S. Luke · 2005
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Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
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The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
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Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis · 2015
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Convolutional networks on graphs for learning molecular fingerprints
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. G. nad John Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis · 2016
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Learning to communicate with deep multi-agent reinforcement learning
J. N. Foerster, Y. M. Assael, N. de Freitas, and S. Whiteson · 2016
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Interaction networks for learning about objects, relations and physics
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Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2016
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Mastering the game of go without human knowledge
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. van den Driessche, T. Graepel, and D. Hassabis · 2017
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Dynamic self-optimization of the antenna tilt for best trade-off between coverage and capacity in mobile networks
N. Dandanov, H. Al-Shatri, A. Klein, and V. Poulkov · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
R. Lowe, Y. Wu, A. Tamar, J. Harb, P. Abbeel, and I. Mordatch · 2017
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Stabilising experience replay for deep multi-agent reinforcement learning
J. Foerster, N. Nardelli, G. Farquhar, T. Afouras, P. H. S. Torr, P. Kohli, and S. Whiteson · 2017
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Deep reinforcement learning that matters
P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup, and D. Meger · 2017
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Emergence of grounded compositional language in multi-agent populations
I. Mordatch and P. Abbeel · 2017
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Deep sets
M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. R. Salakhutdinov, and A. J. Smola · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
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Modeling relational data with graph convolutional networks
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling · 2018
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M. Zhang and Y. Chen · 2018
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Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering
M. Narasimhan, S. Lazebnik, and A. G. Schwing · 2018
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An end-to-end deep learning architecture for graph classification
M. Zhang, Z. Cui, M. Neumann, and Y. Chen · 2018
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Hierarchical graph representation learning with differentiable pooling
Z. Ying, J. You, C. Morris, X. Ren, W. Hamilton, and J. Leskovec · 2018
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Neural relational inference for interacting systems
T. Kipf, E. Fetaya, K.-C. Wang, M. Welling, and R. Zemel · 2018
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Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Inductive representation learning on large graphs
W. Hamilton, Z. Ying, and J. Leskovec · 2017
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Vain: Attentional multi-agent predictive modeling
Y. Hoshen · 2017
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Value-decomposition networks for cooperative multi-agent learning based on team reward
P. Sunehag, G. Lever, A. Gruslys, W. M. Czarnecki, V. Zambaldi, M. Jaderberg, M. Lanctot, N. Sonnerat, J. Z. Leibo, K. Tuyls, and T. Graepel · 2018
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Credit assignment for collective multiagent rl with global rewards
D. T. Nguyen, A. Kumar, and H. C. Lau · 2018
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Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
T. Rashid, M. Samvelyan, C. S. de Witt, G. Farquhar, J. Foerster, and S. Whiteson · 2018
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Multi-agent deep deterministic policy gradient, 2018
R. Lowe, Y. Wu, A. Tamar, J. Harb, P. Abbeel, and I. Mordatch · 2018
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Actor-attention-critic for multi-agent reinforcement learning
S. Iqbal and F. Sha · 2019
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Learning to schedule communication in multi-agent reinforcement learning
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Factor Graph Attention
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Diverse generation for multi-agent sports games
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