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Multi-agent path finding (MAPF) is an indispensable component of large-scale robot deployments in numerous domains ranging from airport management to warehouse automation.
Q. Li, F. Gama, A. Ribeiro, and A. Prorok, “Graph neural networks for decentralized multi-robot path planning,” in arXiv , 2019, pp. 1901–1903
1903
Earlier work this paper cites.
P. E. Hart, N. J. Nilsson, and B. Raphael, “A Formal Basis for the Heuristic Determination of Minimum Cost Paths,” IEEE Transactions on Systems Science and Cybernetics , vol. 4, no. 2, pp. 100–107, 1968
1968
Earlier work this paper cites.
M. Erdmann and T. Lozano-Pérez, “On multiple moving objects,” Algorithmica , vol. 2, no. 1-4, pp. 477–521, 1987
1987
Earlier work this paper cites.
D. Silver, “Cooperative pathfinding,” in Proc. of the Artificial Intelligence and Interactive Digital Entertainment Conf. , Marina del Rey, California, USA, 2005, pp. 117–122
2005
Earlier work this paper cites.
M. Saha and P. Isto, “Multi-robot motion planning by incremental coordination,” in Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems , 2006, pp. 5960–5963
2006
Earlier work this paper cites.
Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in Proc. of the 26th Int. Conf. On Machine Learning, ICML 2009 , 2009, pp. 41–48
2009
Earlier work this paper cites.
T. Standley, “Finding optimal solutions to cooperative pathfinding problems,” in Proc. of AAAI , vol. 1, 2010, pp. 173–178
2010
Earlier work this paper cites.
K. Nagorny, A. W. Colombo, and U. Schmidtmann, “A service- and multi-agent-oriented manufacturing automation architecture: An IEC 62264 level 2 compliant implementation,” Computers in Industry , vol. 63, no. 8, pp. 813–823, 2012
2012
Earlier work this paper cites.
G. Wagner and H. Choset, “Subdimensional expansion for multirobot path planning,” Artificial Intelligence , vol. 219, pp. 1–24, 2015. [Online]. Available: http://dx.doi.org/10.1016/j.artint.2014.11.001
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint 1409.1556 , 2014
2014
Earlier work this paper cites.
J. Berger and N. Lo, “An innovative multi-agent search-and-rescue path planning approach,” Computers and Operations Research , vol. 53, pp. 24–31, 2015
2015
Earlier work this paper cites.
M. Čáp, J. Vokřínek, and A. Kleiner, “Complete decentralized method for on-line multi-robot trajectory planning in well-formed infrastructures,” in Proceedings Int. Conf. on Automated Planning and Scheduling, ICAPS , vol. 2015-January, 2015, pp. 324–332
2015
Cited alongside, same era.
G. Sharon, R. Stern, A. Felner, and N. R. Sturtevant, “Conflict-based search for optimal multi-agent pathfinding,” Artificial Intelligence , vol. 219, pp. 40–66, 2015
2015
Cited alongside, same era.
V. Mnih, A. P. Badia, L. Mirza, A. Graves, T. Harley, T. P. Lillicrap, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in Proc. of the Int. Conf. on Machine Learning , vol. 4, 2016, pp. 2850–2869
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition , jun 2016
2016
Cited alongside, same era.
V. Nguyen, P. Obermeier, T. C. Son, T. Schaub, and W. Yeoh, “Generalized target assignment and path finding using answer set programming,” in Proc. of the Int. Symposium on Combinatorial Search, SoCS 2019 , 2019, pp. 194–195
2019
Later among the works it cites.
J. Švancara, M. Vlk, R. Stern, D. Atzmon, and R. Barták, “Online multi-agent pathfinding,” in Proc. of the National Conf. on Artificial Intelligence, AAAI , vol. 33, 2019, pp. 7732–7739
2019
Later among the works it cites.
G. Sartoretti, J. Kerr, Y. Shi, G. Wagner, T. K. Satish Kumar, S. Koenig, and H. Choset, “PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning,” IEEE Robotics and Automation Letters , vol. 4, no. 3, pp. 2378–2385, 2019
2019
Later among the works it cites.
G. Sartoretti, S. Koenig, and H. Choset, “A Combined Learning- and Search-based Approach to Complete Multi-Agent Path Finding,” in Proc. of the IJCAI workshop on MAPF , 2019
2019
Later among the works it cites.
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H. Ma, J. Li, T. K. Satish Kumar, and S. Koenig, “Lifelong multi-agent path finding for online pickup and delivery tasks,” arXiv , 2017
2017
Cited alongside, same era.
P. Moritz, R. Nishihara, S. Wang, A. Tumanov, R. Liaw, E. Liang, M. Elibol, Z. Yang, W. Paul, M. I. Jordan, and I. Stoica, “Ray: A distributed framework for emerging ai applications,” 2017
2017
Cited alongside, same era.
M. Babaeizadeh, I. Frosio, S. Tyree, J. Clemons, and J. Kautz, “Reinforcement learning through asynchronous advantage actor-critic on a GPU,” Proc. of the Int. Conf. on Learning Representations , 2017
2017
Cited alongside, same era.
T. Hester, M. Vecerik, O. Pietquin, M. Lanctot, T. Schaul, B. Piot, D. Horgan, J. Quan, A. Sendonaris, I. Osband, G. Dulac-Arnold, J. Agapiou, J. Z. Leibo, and A. Gruslys, “Deep q-learning from demonstrations,” arXiv , 2017
2017
Cited alongside, same era.
Q. Wan, C. Gu, S. Sun, M. Chen, H. Huang, and X. Jia, “Lifelong Multi-Agent Path Finding in A Dynamic Environment,” in Proc. of the Int. Conf. on Control, Automation, Robotics and Vision . IEEE, 2018, pp. 875–882
2018
Cited alongside, same era.
Y. Zhu, Z. Wang, J. Merel, A. Rusu, T. Erez, S. Cabi, S. Tunyasuvunakool, J. Kramár, R. Hadsell, N. de Freitas, and N. Heess, “Reinforcement and imitation learning for diverse visuomotor skills,” arXiv , 2018
2018
Cited alongside, same era.
M. Liu, H. Ma, J. Li, and S. Koenig, “Task and path planning for multi-agent pickup and delivery,” in Proc. of the International Joint Conf. on Autonomous Agents and Multiagent Systems, AAMAS , vol. 2, 2019, pp. 1152–1160
2019
Cited alongside, same era.
R. Stern, N. R. Sturtevant, A. Felner, S. Koenig, H. Ma, T. T. Walker, J. Li, D. Atzmon, L. Cohen, T. K. S. Kumar, E. Boyarski, and R. Bartak, “Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks,” Proc. of SoCS , pp. 151–158, 2019
2019
Later among the works it cites.
2020
Closest in time.
S. Mohanty, F. Laurent, N. Bhattacharya, J. Watson, M. Schneider, E. Nygren, C. Eichenberger, C. Baumberger, C. Scheller, A. Egli, G. Vienken, I. Sturm, G. Sartoretti, and G. Spigler, “Flatland-RL: Multi-agent reinforcement learning on trains,” arXiv , 2020
2020
Closest in time.
D. Roost, R. Meier, S. Huschauer, E. Nygren, A. Egli, A. Weiler, and T. Stadelmann, “Improving sample efficiency and multi-agent communication in RL-based train rescheduling,” arXiv , 2020
2020
Closest in time.
J. Li, G. Gange, D. Harabor, P. J. Stuckey, H. Ma, and S. Koenig, “New techniques for pairwise symmetry breaking in multi-agent path finding,” in Proc. of ICAPS , vol. 30, 2020, pp. 193–201
2020
Closest in time.
Y. Zhang, Y. Qian, Y. Yao, H. Hu, and Y. Xu, “Learning to cooperate: Application of deep reinforcement learning for online AGV path finding,” in Proc. of AAMAS , vol. 2020-May, 2020, pp. 2077–2079
2079
Closest in time.