Fetching the paper…
Reading the bibliography…
Multi-agent pathfinding (MAPF) has been widely used to solve large-scale real-world problems, e.g., automation warehouses.
E. Michael and T. Lozano-Perez, “On multiple moving objects,” Algorithmica , vol. 2, no. 1, pp. 477–521, 1987
1987
Earlier work this paper cites.
S. Buckley, “Fast motion planning for multiple moving robots,” in ICRA , 1989
1989
Earlier work this paper cites.
P. A. O’Donnell and T. Lozano-Perez, “Deadlock-free and collision-free coordination of two robot manipulators,” in ICRA , 1989
1989
Earlier work this paper cites.
C. W. Warren, “Multiple robot path coordination using artificial potential fields,” in ICRA , 1990
1990
Earlier work this paper cites.
K. Azarm and G. Schmidt, “Conflict-free motion of multiple mobile robots based on decentralized motion planning and negotiation,” in ICRA , 1997
1997
Earlier work this paper cites.
C. Ferrari, E. Pagello, J. Ota, and T. Arai, “Multirobot motion coordination in space and time,” Robotics and Autonomous Systems , vol. 25, pp. 219–229, 1998
1998
Earlier work this paper cites.
M. Bennewitz, W. Burgard, and S. Thrun, “Finding and optimizing solvable priority schemes for decoupled path planning techniques for teams of mobile robots,” Robotics and Autonomous Systems , vol. 41, pp. 89–99, 2002
2002
Earlier work this paper cites.
E. A. Hansen, D. S. Bernstein, and S. Zilberstein, “Dynamic programming for partially observable stochastic games,” in AAAI , 2004
2004
Earlier work this paper cites.
D. Silver, “Cooperative pathfinding,” in AIIDE , 2005
2005
Earlier work this paper cites.
J. V. D. Berg and M. Overmars, “Prioritized motion planning for multiple robots,” in IROS , 2005
2005
Earlier work this paper cites.
A. Howard, L. Parker, and G. Sukhatme, “Experiments with a large heterogeneous mobile robot team: Exploration, mapping, deployment and detection,” The International Journal of Robotics Research , vol. 25, pp. 431–447, 2006
2006
Earlier work this paper cites.
N. R. Sturtevant and M. Buro, “Improving collaborative pathfinding using map abstraction,” in AIIDE , 2006
2006
Earlier work this paper cites.
Y. Shoham and K. Leyton-Brown, Multiagent systems: Algorithmic, game-theoretic, and logical foundations . Cambridge University Press, 2008
2008
Earlier work this paper cites.
J. V. D. Berg, S. Guy, M. Lin, and D. Manocha, “Reciprocal n-body collision avoidance,” in ISRR , 2009
2009
Earlier work this paper cites.
M. Rezaee and M. Yaghmaee, “Cluster based routing protocol for mobile ad hoc networks,” INFOCOM , vol. 8, no. 1, pp. 30–36, 2009
2009
Earlier work this paper cites.
P. Velagapudi, K. Sycara, and P. Scerri, “Decentralized prioritized planning in large multirobot teams,” in IROS , 2010
2010
Cited alongside, same era.
K. Wang and A. Botea, “MAPP: a scalable multi-agent path planning algorithm with tractability and completeness guarantees,” Journal of Artificial Intelligence Research , vol. 42, pp. 55–90, 2011
2011
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, 2012
2012
Cited alongside, same era.
C. Ferner, G. Wagner, and H. Choset, “ODrM* optimal multirobot path planning in low dimensional search spaces,” in ICRA , 2013
2013
Cited alongside, same era.
M. Rubenstein, A. Cornejo, and R. Nagpal, “Programmable self-assembly in a thousand-robot swarm,” Science , vol. 345, pp. 795–799, 2014
A. Peysakhovich and A. Lerer, “Consequentialist conditional cooperation in social dilemmas with imperfect information,” in AAAI Workshops , 2018
2018
Later among the works it cites.
——, “Prosocial learning agents solve generalized stag hunts better than selfish ones,” in AAMAS , 2018
2018
Later among the works it cites.
E. Hughes, J. Z. Leibo, M. Phillips, K. Tuyls, E. A. Duéñez-Guzmán, A. Castañeda, I. Dunning, T. Zhu, K. R. McKee, R. Koster, H. Roff, and T. Graepel, “Inequity aversion improves cooperation in intertemporal social dilemmas,” in NeurIPS , 2018
2018
Later among the works it cites.
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in ICML , 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
S. Levine and V. Koltun, “Learning complex neural network policies with trajectory optimization,” in ICML , 2014
2014
Cited alongside, same era.
S. Levine and P. Abbeel, “Learning neural network policies with guided policy search under unknown dynamics,” in NeurIPS , 2014
2014
Cited alongside, same era.
L. V. D. Maaten, “Accelerating t-sne using tree-based algorithms,” J. Mach. Learn. Res. , vol. 15, pp. 3221–3245, 2014
2014
Cited alongside, same era.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. A. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, “Human-level control through deep reinforcement learning,” Nature , vol. 518, pp. 529–533, 2015
2015
Cited alongside, same era.
G. Wagner and H. Choset, “Subdimensional expansion for multirobot path planning,” Artificial Intelligence , vol. 219, pp. 1–24, 2015
2015
Cited alongside, same era.
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in ICML , 2016
2016
Cited alongside, same era.
2017
Cited alongside, same era.
2019
Later among the works it cites.
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. Barták, “Multi-agent pathfinding: Definitions, variants, and benchmarks,” in SOCS , 2019
2019
Later among the works it cites.
R. Lowe, J. Foerster, Y. Boureau, J. Pineau, and Y. Dauphin, “On the pitfalls of measuring emergent communication,” in AAMAS , 2019
2019
Later among the works it cites.
H. Ma, D. Harabor, P. Stuckey, J. Li, and S. Koenig, “Searching with consistent prioritization for multi-agent path finding,” in SOCS , 2019
2019
Later among the works it cites.
N. Jaques, A. Lazaridou, E. Hughes, Çaglar Gülçehre, P. A. Ortega, D. Strouse, J. Z. Leibo, and N. D. Freitas, “Social influence as intrinsic motivation for multi-agent deep reinforcement learning,” in ICML , 2019
2019
Later among the works it cites.
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 AAMAS , 2020
2020
Later among the works it cites.
L. Zhiyao and G. Sartoretti, “Deep reinforcement learning based multi-agent pathfinding,” Technical Report. , 2020
2020
Later among the works it cites.
B. Freed, G. Sartoretti, and H. Choset, “Simultaneous policy and discrete communication learning for multi-agent cooperation,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 2498–2505, 2020
2020
Later among the works it cites.
J.-B. Grill, F. Strub, F. Altch’e, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. Guo, M. G. Azar, B. Piot, K. Kavukcuoglu, R. Munos, and M. Valko, “Bootstrap your own latent: A new approach to self-supervised learning,” in NeurIPS , 2020
2020
Later among the works it cites.
M. Damani, Z. Luo, E. Wenzel, and G. Sartoretti, “Primal 2 : Pathfinding via reinforcement and imitation multi-agent learning - lifelong,” IEEE Robotics and Automation Letters , vol. 6, pp. 2666–2673, 2021
2021
Later among the works it cites.
Z. Ma, Y. Luo, and H. Ma, “Distributed heuristic multi-agent path finding with communication,” in ICRA , 2021
2021
Later among the works it cites.