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This paper proposes a distributed Multi-Agent Reinforcement Learning (MARL) algorithm for a team of Unmanned Aerial Vehicles (UAVs).
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M. Schwager, D. Rus, and J.-J. Slotine, “Decentralized, adaptive coverage control for networked robots,” The International Journal of Robotics Research , vol. 28, no. 3, pp. 357–375, 2009
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A. Breitenmoser, M. Schwager, J.-C. Metzger, R. Siegwart, and D. Rus, “Voronoi coverage of non-convex environments with a group of networked robots,” in Robotics and Automation (ICRA), 2010 IEEE International Conference on . IEEE, 2010, pp. 4982–4989
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H. Bou-Ammar, H. Voos, and W. Ertel, “Controller design for quadrotor uavs using reinforcement learning,” in Control Applications (CCA), 2010 IEEE International Conference on . IEEE, 2010, pp. 2130–2135
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H. M. La, W. Sheng, and J. Chen, “Cooperative and active sensing in mobile sensor networks for scalar field mapping,” IEEE Transactions on Systems, Man, and Cybernetics: Systems , vol. 45, no. 1, pp. 1–12, 2015
2015
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H. M. La, R. Lim, and W. Sheng, “Multirobot cooperative learning for predator avoidance,” IEEE Transactions on Control Systems Technology , vol. 23, no. 1, pp. 52–63, 2015
2015
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2016
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A. A. Adepegba, M. S. Miah, and D. Spinello, “Multi-agent area coverage control using reinforcement learning.” in FLAIRS Conference , 2016, pp. 368–373
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L. Busoniu, R. Babuska, B. De Schutter, and D. Ernst, Reinforcement learning and dynamic programming using function approximators . CRC press, 2010, vol. 39
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M. Schwager, B. J. Julian, M. Angermann, and D. Rus, “Eyes in the sky: Decentralized control for the deployment of robotic camera networks,” Proceedings of the IEEE , vol. 99, no. 9, pp. 1541–1561, 2011
2011
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T. Tomic, K. Schmid, P. Lutz, A. Domel, M. Kassecker, E. Mair, I. L. Grixa, F. Ruess, M. Suppa, and D. Burschka, “Toward a fully autonomous uav: Research platform for indoor and outdoor urban search and rescue,” IEEE robotics & automation magazine , vol. 19, no. 3, pp. 46–56, 2012
2012
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H. M. La and W. Sheng, “Dynamic target tracking and observing in a mobile sensor network,” Robotics and Autonomous Systems , vol. 60, no. 7, pp. 996 – 1009, 2012. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0921889012000565
2012
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A. Nowé, P. Vrancx, and Y.-M. De Hauwere, “Game theory and multi-agent reinforcement learning,” in Reinforcement Learning . Springer, 2012, pp. 441–470
2012
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H. M. La and W. Sheng, “Distributed sensor fusion for scalar field mapping using mobile sensor networks,” IEEE Transactions on cybernetics , vol. 43, no. 2, pp. 766–778, 2013
2013
Cited alongside, same era.
A. Faust, I. Palunko, P. Cruz, R. Fierro, and L. Tapia, “Learning swing-free trajectories for uavs with a suspended load,” in Robotics and Automation (ICRA), 2013 IEEE International Conference on . IEEE, 2013, pp. 4902–4909
2013
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2013
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M. T. Nguyen, H. M. La, and K. A. Teague, “Collaborative and compressed mobile sensing for data collection in distributed robotic networks,” IEEE Transactions on Control of Network Systems , vol. PP, no. 99, pp. 1–1, 2017
2017
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H. X. Pham, H. M. La, D. Feil-Seifer, and M. Deans, “A distributed control framework for a team of unmanned aerial vehicles for dynamic wildfire tracking,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Sept 2017, pp. 6648–6653
2017
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T. Nguyen, H. M. La, T. D. Le, and M. Jafari, “Formation control and obstacle avoidance of multiple rectangular agents with limited communication ranges,” IEEE Transactions on Control of Network Systems , vol. 4, no. 4, pp. 680–691, Dec 2017
2017
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F. Muñoz, E. S. Espinoza Quesada, H. M. La, S. Salazar, S. Commuri, and L. R. Garcia Carrillo, “Adaptive consensus algorithms for real-time operation of multi-agent systems affected by switching network events,” International Journal of Robust and Nonlinear Control , vol. 27, no. 9, pp. 1566–1588, 2017, rnc.3687. [Online]. Available: http://dx.doi.org/10.1002/rnc.3687
2017
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2017
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A. K. Sadhu and A. Konar, “Improving the speed of convergence of multi-agent q-learning for cooperative task-planning by a robot-team,” Robotics and Autonomous Systems , vol. 92, pp. 66–80, 2017
2017
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S.-M. Hung and S. N. Givigi, “A q-learning approach to flocking with uavs in a stochastic environment,” IEEE transactions on cybernetics , vol. 47, no. 1, pp. 186–197, 2017
2017
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Z. Zhang, D. Zhao, J. Gao, D. Wang, and Y. Dai, “Fmrq—a multiagent reinforcement learning algorithm for fully cooperative tasks,” IEEE transactions on cybernetics , vol. 47, no. 6, pp. 1367–1379, 2017
2017
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