Fetching the paper…
Reading the bibliography…
Multi-Agent Reinforcement Learning (MARL) has enjoyed significant recent progress thanks, in part, to the integration of deep learning techniques for modeling interactions in complex environments.
M. J. Matarić, “Reinforcement learning in the multi-robot domain,” Robot colonies , pp. 73–83, 1997
1997
Earlier work this paper cites.
D. S. Bernstein, R. Givan, N. Immerman, and S. Zilberstein, “The complexity of decentralized control of markov decision processes,” Mathematics of operations research , vol. 27, no. 4, pp. 819–840, 2002
2002
Earlier work this paper cites.
N. Koenig and A. Howard, “Design and use paradigms for gazebo, an open-source multi-robot simulator,” in 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566) , vol. 3, 2004, pp. 2149–2154 vol.3
2004
Earlier work this paper cites.
2006
Earlier work this paper cites.
D. Pickem, M. Lee, and M. Egerstedt, “The gritsbot in its natural habitat - a multi-robot testbed,” in 2015 IEEE International Conference on Robotics and Automation (ICRA) , 2015, pp. 4062–4067
2015
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
P. Sunehag, G. Lever, A. Gruslys, W. M. Czarnecki, V. Zambaldi, M. Jaderberg, M. Lanctot, N. Sonnerat, J. Z. Leibo, K. Tuyls, and T. Graepel, “Value-decomposition networks for cooperative multi-agent learning,” 2017
2017
Earlier work this paper cites.
L. Wang, A. D. Ames, and M. Egerstedt, “Safety barrier certificates for collisions-free multirobot systems,” IEEE Transactions on Robotics , vol. 33, no. 3, pp. 661–674, 2017
2017
Earlier work this paper cites.
PACE, Partnership for an Advanced Computing Environment (PACE) , 2017. [Online]. Available: http://www.pace.gatech.edu
2017
Earlier work this paper cites.
R. N. Haksar and M. Schwager, “Distributed deep reinforcement learning for fighting forest fires with a network of aerial robots,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 1067–1074
2018
Earlier work this paper cites.
H. Bae, G. Kim, J. Kim, D. Qian, and S. Lee, “Multi-robot path planning method using reinforcement learning,” Applied sciences , vol. 9, no. 15, p. 3057, 2019
2019
Cited alongside, same era.
J. Lin, X. Yang, P. Zheng, and H. Cheng, “End-to-end decentralized multi-robot navigation in unknown complex environments via deep reinforcement learning,” in 2019 IEEE International Conference on Mechatronics and Automation (ICMA) . IEEE, 2019, pp. 2493–2500
2019
Cited alongside, same era.
2019
Cited alongside, same era.
R. Stern, N. Sturtevant, A. Felner, S. Koenig, H. Ma, T. Walker, J. Li, D. Atzmon, L. Cohen, T. Kumar et al. , “Multi-agent pathfinding: Definitions, variants, and benchmarks,” in Proceedings of the International Symposium on Combinatorial Search , 2019
2019
Z. Zong, M. Zheng, Y. Li, and D. Jin, “Mapdp: Cooperative multi-agent reinforcement learning to solve pickup and delivery problems,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 9, 2022, pp. 9980–9988
2022
Later among the works it cites.
E. Seraj, Z. Wang, R. Paleja, D. Martin, M. Sklar, A. Patel, and M. Gombolay, “Learning efficient diverse communication for cooperative heterogeneous teaming,” in International conference on autonomous agents and multiagent systems , 2022
2022
Later among the works it cites.
A. Alagha, S. Singh, R. Mizouni, J. Bentahar, and H. Otrok, “Target localization using multi-agent deep reinforcement learning with proximal policy optimization,” Future Generation Computer Systems , vol. 136, pp. 342–357, 2022
2022
Later among the works it cites.
2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Q. Li, F. Gama, A. Ribeiro, and A. Prorok, “Graph neural networks for decentralized multi-robot path planning,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 11 785–11 792
2020
Cited alongside, same era.
C. Sun, M. Shen, and J. P. How, “Scaling up multiagent reinforcement learning for robotic systems: Learn an adaptive sparse communication graph,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 11 755–11 762
2020
Cited alongside, same era.
R. Han, S. Chen, and Q. Hao, “Cooperative multi-robot navigation in dynamic environment with deep reinforcement learning,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 448–454
2020
Cited alongside, same era.
S. Wilson, P. Glotfelter, L. Wang, S. Mayya, G. Notomista, M. Mote, and M. Egerstedt, “The robotarium: Globally impactful opportunities, challenges, and lessons learned in remote-access, distributed control of multirobot systems,” IEEE Control Systems Magazine , 2020
2020
Cited alongside, same era.
T. Rashid, M. Samvelyan, C. S. De Witt, G. Farquhar, J. Foerster, and S. Whiteson, “Monotonic value function factorisation for deep multi-agent reinforcement learning,” J. Mach. Learn. Res. , vol. 21, no. 1, jan 2020
2020
Cited alongside, same era.
G. Neville, A. Messing, H. Ravichandar, S. Hutchinson, and S. Chernova, “An interleaved approach to trait-based task allocation and scheduling,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 1507–1514
2021
Cited alongside, same era.
H. Zhang, J. Cheng, L. Zhang, Y. Li, and W. Zhang, “H2gnn: Hierarchical-hops graph neural networks for multi-robot exploration in unknown environments,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 3435–3442, 2022
2022
Cited alongside, same era.
Later among the works it cites.
Z. Liang, J. Cao, S. Jiang, D. Saxena, J. Chen, and H. Xu, “From multi-agent to multi-robot: A scalable training and evaluation platform for multi-robot reinforcement learning,” 06 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
C. Yu, A. Velu, E. Vinitsky, J. Gao, Y. Wang, A. Bayen, and Y. WU, “The surprising effectiveness of ppo in cooperative multi-agent games,” in Advances in Neural Information Processing Systems , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., vol. 35. Curran Associates, Inc., 2022, pp. 24 611–24 624. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2022/file\/9c1535a02f0ce079433344e14d910597-Paper-Datasets_and_Benchmarks.pdf
2022
Later among the works it cites.
J. Orr and A. Dutta, “Multi-agent deep reinforcement learning for multi-robot applications: A survey,” Sensors , vol. 23, no. 7, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
P. Howell, M. Rudolph, R. J. Torbati, K. Fu, and H. Ravichandar, “Generalization of heterogeneous multi-robot policies via awareness and communication of capabilities,” in 7th Annual Conference on Robot Learning , 2023. [Online]. Available: https://openreview.net/forum?id=N3VbFUpwaa
2023
Closest in time.