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Multi-Agent Reinforcement Learning (MARL) algorithms show amazing performance in simulation in recent years, but placing MARL in real-world applications may suffer safety problems.
S. Prajna, A. Jadbabaie, and G. J. Pappas, “A Framework for Worst-Case and Stochastic Safety Verification Using Barrier Certificates,” IEEE Transactions on Automatic Control , vol. 52, no. 8, pp. 1415–1428, 2007
2007
Earlier work this paper cites.
D. Wilkie, J. v. d. Berg, and D. Manocha, “Generalized velocity obstacles,” in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems , 2009, pp. 5573–5578
2009
Earlier work this paper cites.
B. J. Goode and M. J. Roan, “A Differential Game Theoretic Approach for Two-Agent Collision Avoidance with Travel Limitations,” Journal of Intelligent & Robotic Systems , vol. 67, no. 3, pp. 201–218, 2012
2012
Earlier work this paper cites.
J. Garcia and F. Fernández, “Safe exploration of state and action spaces in reinforcement learning,” Journal of Artificial Intelligence Research , vol. 45, pp. 515–564, 2012
2012
Earlier work this paper cites.
A. D. Ames, J. W. Grizzle, and P. Tabuada, “Control barrier function based quadratic programs with application to adaptive cruise control,” in 53rd IEEE Conference on Decision and Control , 2014, pp. 6271–6278
2014
Earlier work this paper cites.
U. Borrmann, L. Wang, A. D. Ames, and M. Egerstedt, “Control Barrier Certificates for Safe Swarm Behavior,” IFAC-PapersOnLine , vol. 48, no. 27, pp. 68–73, 2015
2015
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature , vol. 529, p. 484, 1 2016
2016
Earlier work this paper cites.
R. Lowe, Y. Wu, A. Tamar, J. Harb, P. Abbeel, and I. Mordatch, “Multi-agent actor-critic for mixed cooperative-competitive environments,” Advances in Neural Information Processing Systems , vol. 2017-Decem, pp. 6380–6391, 2017
2017
Earlier work this paper cites.
P. Glotfelter, J. Cortés, and M. Egerstedt, “Nonsmooth Barrier Functions With Applications to Multi-Robot Systems,” IEEE Control Systems Letters , vol. 1, no. 2, pp. 310–315, 2017
2017
Cited alongside, same era.
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
Cited alongside, same era.
M. Alshiekh, R. Bloem, R. Ehlers, B. Könighofer, S. Niekum, and U. Topcu, “Safe Reinforcement Learning via Shielding,” in AAAI , 2018
2018
Cited alongside, same era.
J. Hwangbo, J. Lee, A. Dosovitskiy, D. Bellicoso, V. Tsounis, V. Koltun, and M. Hutter, “Learning agile and dynamic motor skills for legged robots,” Science Robotics , vol. 4, no. 26, 2019
2019
Cited alongside, same era.
G. Zhang, Y. Li, X. Xu, and H. Dai, “Efficient Training Techniques for Multi-Agent Reinforcement Learning in Combat Tasks,” IEEE Access , vol. 7, pp. 109 301–109 310, 2019
2019
Later among the works it cites.
R. Cheng, M. J. Khojasteh, A. D. Ames, and J. W. Burdick, “Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties,” in 2020 59th IEEE Conference on Decision and Control (CDC) , 2020, pp. 777–783
2020
Later among the works it cites.
J. Hu, M. Wang, C. Zhao, Q. Pan, and C. Du, “Formation control and collision avoidance for multi-UAV systems based on Voronoi partition,” Science China Technological Sciences , vol. 63, no. 1, pp. 65–72, 2020
2020
Later among the works it cites.
Z. Marvi and B. Kiumarsi, “Safe reinforcement learning: A control barrier function optimization approach,” International Journal of Robust and Nonlinear Control , p. 1– 18, 8 2020
2020
Later among the works it cites.
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M. Samvelyan, T. Rashid, C. S. De Witt, G. Farquhar, N. Nardelli, T. G. Rudner, C. M. Hung, P. H. Torr, J. Foerster, and S. Whiteson, “The StarCraft multi-agent challenge,” Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS , vol. 4, no. NeurIPS, pp. 2186–2188, 2019
2019
Cited alongside, same era.
J. F. Fisac, A. K. Akametalu, M. N. Zeilinger, S. Kaynama, J. Gillula, and C. J. Tomlin, “A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems,” IEEE Transactions on Automatic Control , vol. 64, no. 7, pp. 2737–2752, 2019
2019
Cited alongside, same era.
R. Cheng, G. Orosz, R. M. Murray, and J. W. Burdick, “End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks,” 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 , pp. 3387–3395, 2019
2019
Cited alongside, same era.
A. D. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada, “Control Barrier Functions: Theory and Applications,” in 2019 18th European Control Conference (ECC) , 2019, pp. 3420–3431
2019
Cited alongside, same era.
Y. Wang, L. Dong, and C. Sun, “Cooperative control for multi-player pursuit-evasion games with reinforcement learning,” Neurocomputing , vol. 412, pp. 101–114, 2020
2020
Later among the works it cites.
H. Xiong and X. Diao, “Safety Robustness of Reinforcement Learning Policies: A View from Robust Control,” Neurocomputing , vol. 422, pp. 12–21, 2021
2021
Closest in time.
I. Elsayed-Aly, S. Bharadwaj, C. Amato, R. Ehlers, U. Topcu, and L. Feng, “Safe Multi-Agent Reinforcement Learning via Shielding,” ArXiv , vol. abs/2101.1, 2021
2021
Closest in time.