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This letter proposes a novel reinforcement learning method for the synthesis of a control policy satisfying a control specification described by a linear temporal logic formula.
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Q. Gao, D. Hajinezhad, Y. Zhang, Y. Kantaros, and M. M. Zavlanos, “Reduced variance deep reinforcement learning with temporal logic specifications,” in Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems , 2019, pp. 237–248
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X. Li, Z. Serlin, G. Yang, and C. Belta, “A formal methods approach to interpretable reinforcement learning for robotic planning,” Science Robotics , vol. 4, no. 37, pp. 1–16, 2019
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E. M. Hahn, M. Perez, S. Schewe, F. Somenzi, A. Trivedi, and D. Wojtczak, “Omega-regular objectives in model-free reinforcement learning,” in International Conference on Tools and Algorithms for the Construction and Analysis of Systems . Springer, 2019, pp. 395–412
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2018
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L. Breuer, “Introduction to stochastic processes,” [Online]. Available: https://www.kent.ac.uk/smsas/personal/lb209/files/sp07.pdf
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