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Many real-world problems require the coordination of multiple autonomous agents.
Dynamic programming and optimal control , volume 1
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Independent reinforcement learners in cooperative markov games: a survey regarding coordination problems
L. Matignon, G. J. Laurent, and N. Le Fort-Piat · 2012
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C. Zhang and V. Lesser · 2013
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A survey on coverage path planning for robotics
E. Galceran and M. Carreras · 2013
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Learning multiagent communication with backpropagation
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Interaction networks for learning about objects, relations and physics
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C. Wu, A. Kreidieh, E. Vinitsky, and A. M. Bayen · 2017
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Cooperative multi-agent control using deep reinforcement learning
J. K. Gupta, M. Egorov, and M. Kochenderfer · 2017
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Deep decentralized multi-task multi-agent reinforcement learning under partial observability
S. Omidshafiei, J. Pazis, C. Amato, J. P. How, and J. Vian · 2017
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Multi-agent reinforcement learning in sequential social dilemmas
J. Z. Leibo, V. Zambaldi, M. Lanctot, J. Marecki, and T. Graepel · 2017
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A multi-agent reinforcement learning model of common-pool resource appropriation
J. Perolat, J. Z. Leibo, V. Zambaldi, C. Beattie, K. Tuyls, and T. Graepel · 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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Emergence of grounded compositional language in multi-agent populations
I. Mordatch and P. Abbeel · 2018
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Ray: A distributed framework for emerging ai applications
P. Moritz, R. Nishihara, S. Wang, A. Tumanov, R. Liaw, E. Liang, M. Elibol, Z. Yang, W. Paul, M. I. Jordan, and I. Stoica · 2018
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RLlib: Abstractions for distributed reinforcement learning
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Convolutional neural network architectures for signals supported on graphs
F. Gama, A. G. Marques, G. Leus, and A. Ribeiro · 2018
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A fleet of miniature cars for experiments in cooperative driving
N. Hyldmar, Y. He, and A. Prorok · 2019
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Decentralization of multiagent policies by learning what to communicate
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Semi-supervised classification with graph convolutional networks
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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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Graph neural networks for learning robot team coordination. Federated AI for robotics workshop
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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. N. Foerster, and S. Whiteson · 2018
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Counterfactual multi-agent policy gradients
J. N. Foerster, G. Farquhar, T. Afouras, N. Nardelli, and S. Whiteson · 2018
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J. Paulos, S. W. Chen, D. Shishika, and V. Kumar · 2019
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Finding friend and foe in multi-agent games
J. Serrino, M. Kleiman-Weiner, D. C. Parkes, and J. Tenenbaum · 2019
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No-press diplomacy: Modeling multi-agent gameplay
P. Paquette, Y. Lu, S. S. Bocco, M. Smith, O.-G. Satya, J. K. Kummerfeld, J. Pineau, S. Singh, and A. C. Courville · 2019
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Graph policy gradients for large scale robot control
A. Khan, E. Tolstaya, A. Ribeiro, and V. Kumar · 2020
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Graph neural networks for decentralized multi-robot path planning
Q. Li, F. Gama, A. Ribeiro, and A. Prorok · 2020
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Learning decentralized controllers for robot swarms with graph neural networks
E. Tolstaya, F. Gama, J. Paulos, G. Pappas, V. Kumar, and A. Ribeiro · 2020
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