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Multi-agent Pathfinding (MAPF) problem generally asks to find a set of conflict-free paths for a set of agents confined to a graph and is typically solved in a centralized fashion.
Reciprocal velocity obstacles for real-time multi-agent navigation
Van den Berg, J.; Lin, M.; and Manocha, D. 2008 · 1935
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Decentralized motion planning for multiple mobile robots: The cocktail party model
Lumelsky, V. J.; and Harinarayan, K. 1997 · 1997
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Planning and acting in partially observable stochastic domains
Kaelbling, L. P.; Littman, M. L.; and Cassandra, A. R. 1998 · 1998
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The complexity of decentralized control of Markov decision processes
Bernstein, D. S.; Givan, R.; Immerman, N.; and Zilberstein, S. 2002 · 2002
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MAPP: a scalable multi-agent path planning algorithm with tractability and completeness guarantees
Wang, K.-H. C.; and Botea, A. 2011 · 2011
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Push and rotate: cooperative multi-agent path planning
de Wilde, B.; ter Mors, A. W.; and Witteveen, C. 2013 · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J.; Gulcehre, C.; Cho, K.; and Bengio, Y. 2014 · 2014
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Human-level control through deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. a.; Veness, J.; Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A. K.; Ostrovski, G.; Petersen, S.; Beattie, C.; Sadik, A.; Antonoglou, I.; King, H.; Kumaran, D.; Wierstra, D.; Legg, S.; and Hassabis, D. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Learning for Decentralized Control of Multiagent Systems in Large, Partially-Observable Stochastic Environments
Liu, M.; Amato, C.; Anesta, E. P.; Griffith, J. D.; and How, J. P. 2016 · 2016
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Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks
Ma, H.; Li, J.; Kumar, T.; and Koenig, S. 2017 · 2017
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Proximal policy optimization algorithms
Schulman, J.; Wolski, F.; Dhariwal, P.; Radford, A.; and Klimov, O. 2017 · 2017
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QMIX: Monotonic value function factorisation for deep multi-agent reinforcement Learning
Rashid, T.; Samvelyan, M.; De Witt, C. S.; Farquhar, G.; Foerster, J.; and Whiteson, S. 2018 · 2018
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Hierarchical Deep Multiagent Reinforcement Learning
Tang, H.; Hao, J.; Lv, T.; Chen, Y.; Zhang, Z.; Jia, H.; Ren, C.; Zheng, Y.; Fan, C.; and Wang, L. 2018 · 2018
Cited alongside, same era.
Task and path planning for multi-agent pickup and delivery
Liu, M.; Ma, H.; Li, J.; and Koenig, S. 2019 · 2019
Cited alongside, same era.
Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery
Ma, H.; Hönig, W.; Kumar, T. K. S.; Ayanian, N.; and Koenig, S. 2019b · 2019
Cited alongside, same era.
Priority inheritance with backtracking for iterative multi-agent path finding
Okumura, K.; Machida, M.; Défago, X.; and Tamura, Y. 2019 · 2019
Cited alongside, same era.
The StarCraft multi-agent challenge
Samvelyan, M.; Rashid, T.; De Witt, C. S.; Farquhar, G.; Nardelli, N.; Rudner, T. G.; Hung, C. M.; Torr, P. H.; Foerster, J.; and Whiteson, S. 2019 · 2019
Cited alongside, same era.
Lifelong multi-agent path finding in large-scale warehouses
Li, J.; Tinka, A.; Kiesel, S.; Durham, J. W.; Kumar, T. S.; and Koenig, S. 2021 · 2021
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Distributed heuristic multi-agent path finding with communication
Ma, Z.; Luo, Y.; and Ma, H. 2021 · 2021
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Hybrid Policy Learning for Multi-Agent Pathfinding
Skrynnik, A.; Yakovleva, A.; Davydov, V.; Yakovlev, K.; and Panov, A. I. 2021 · 2021
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Multi-Agent Path Finding with Prioritized Communication Learning
Li, W.; Chen, H.; Jin, B.; Tan, W.; Zha, H.; and Wang, X. 2022 · 2022
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Priority inheritance with backtracking for iterative multi-agent path finding
Okumura, K.; Machida, M.; Défago, X.; and Tamura, Y. 2022 · 2022
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Deep Reinforcement Learning: A Survey
Wang, X.; Wang, S.; Liang, X.; Zhao, D.; Huang, J.; Xu, X.; Dai, B.; and Miao, Q. 2022 · 2022
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Primal: Pathfinding via reinforcement and imitation multi-agent learning
Sartoretti, G.; Kerr, J.; Shi, Y.; Wagner, G.; Kumar, T. S.; Koenig, S.; and Choset, H. 2019 · 2019
Cited alongside, same era.
Multi-agent pathfinding: Definitions, variants, and benchmarks
Stern, R.; Sturtevant, N. R.; Felner, A.; Koenig, S.; Ma, H.; Walker, T. T.; Li, J.; Atzmon, D.; Cohen, L.; Kumar, T. S.; et al. 2019 · 2019
Cited alongside, same era.
The MineRL Competition on Sample-Efficient Reinforcement Learning Using Human Priors: A Retrospective
Milani, S.; Topin, N.; Houghton, B.; Guss, W. H.; Mohanty, S. P.; Vinyals, O.; and Kuno, N. S. 2020 · 2020
Cited alongside, same era.
Glas: Global-to-local safe autonomy synthesis for multi-robot motion planning with end-to-end learning
Riviere, B.; Hönig, W.; Yue, Y.; and Chung, S.-J. 2020 · 2020
Cited alongside, same era.
Integrated task assignment and path planning for capacitated multi-agent pickup and delivery
Chen, Z.; Alonso-Mora, J.; Bai, X.; Harabor, D. D.; and Stuckey, P. J. 2021 · 2021
Cited alongside, same era.
PRIMAL _ 2 \_2 : Pathfinding via reinforcement and imitation multi-agent learning-lifelong
Damani, M.; Luo, Z.; Wenzel, E.; and Sartoretti, G. 2021 · 2021
Cited alongside, same era.
Mastering atari with discrete world models
Hafner, D.; Lillicrap, T.; Norouzi, M.; and Ba, J. 2021 · 2021
Cited alongside, same era.
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The surprising effectiveness of ppo in cooperative multi-agent games
Yu, C.; Velu, A.; Vinitsky, E.; Gao, J.; Wang, Y.; Bayen, A.; and Wu, Y. 2022 · 2022
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Decentralized probabilistic multi-robot collision avoidance using buffered uncertainty-aware Voronoi cells
Zhu, H.; Brito, B.; and Alonso-Mora, J. 2022 · 2022
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Mastering diverse domains through world models
Hafner, D.; Pasukonis, J.; Ba, J.; and Lillicrap, T. 2023 · 2023
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PushWorld: A benchmark for manipulation planning with tools and movable obstacles
Kansky, K.; Vaidyanath, S.; Swingle, S.; Lou, X.; Lazaro-Gredilla, M.; and George, D. 2023 · 2023
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SCRIMP: Scalable Communication for Reinforcement-and Imitation-Learning-Based Multi-Agent Pathfinding
Wang, Y.; Xiang, B.; Huang, S.; and Sartoretti, G. 2023 · 2023
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Deep multiagent reinforcement learning: Challenges and directions
Wong, A.; Bäck, T.; Kononova, A. V.; and Plaat, A. 2023 · 2023
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