暂无分享,去创建一个
[1] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[2] Daniel Kroening,et al. Certified Reinforcement Learning with Logic Guidance , 2019, Artif. Intell..
[3] Pieter Abbeel,et al. Benchmarking Deep Reinforcement Learning for Continuous Control , 2016, ICML.
[4] Toshimitsu Ushio,et al. Reinforcement Learning of Control Policy for Linear Temporal Logic Specifications Using Limit-Deterministic Generalized Büchi Automata , 2020, IEEE Control Systems Letters.
[5] Daniel Kroening,et al. Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees , 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).
[6] Eduardo F. Morales,et al. An Introduction to Reinforcement Learning , 2011 .
[7] S. Shankar Sastry,et al. A learning based approach to control synthesis of Markov decision processes for linear temporal logic specifications , 2014, 53rd IEEE Conference on Decision and Control.
[8] K.J. Kyriakopoulos,et al. Automatic synthesis of multi-agent motion tasks based on LTL specifications , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).
[9] Jan Kretínský,et al. Limit-Deterministic Büchi Automata for Linear Temporal Logic , 2016, CAV.
[10] Chris Watkins,et al. Learning from delayed rewards , 1989 .
[11] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[12] Sven Schewe,et al. Omega-Regular Objectives in Model-Free Reinforcement Learning , 2018, TACAS.
[13] Yuval Tassa,et al. Continuous control with deep reinforcement learning , 2015, ICLR.
[14] Mihalis Yannakakis,et al. The complexity of probabilistic verification , 1995, JACM.
[15] Christel Baier,et al. Principles of model checking , 2008 .
[16] Ufuk Topcu,et al. Receding horizon temporal logic planning for dynamical systems , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.
[17] Daniel Kroening,et al. Logically-Constrained Neural Fitted Q-Iteration , 2018, AAMAS.
[18] Ben J. A. Kröse,et al. Learning from delayed rewards , 1995, Robotics Auton. Syst..
[19] Michael M. Zavlanos,et al. Reduced variance deep reinforcement learning with temporal logic specifications , 2019, ICCPS.
[20] Calin Belta,et al. A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks , 2018, 2018 Annual American Control Conference (ACC).
[21] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[22] Jan Kretínský,et al. Owl: A Library for ω-Words, Automata, and LTL , 2018, ATVA.
[23] Jun Liu,et al. Robustly Complete Synthesis of Memoryless Controllers for Nonlinear Systems With Reach-and-Stay Specifications , 2018, IEEE Transactions on Automatic Control.
[24] Guy Lever,et al. Deterministic Policy Gradient Algorithms , 2014, ICML.
[25] Emilio Frazzoli,et al. Sampling-based algorithms for optimal motion planning , 2011, Int. J. Robotics Res..
[26] Lydia E. Kavraki,et al. Sampling-based motion planning with temporal goals , 2010, 2010 IEEE International Conference on Robotics and Automation.
[27] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[28] Fabio Somenzi,et al. Formal Controller Synthesis for Continuous-Space MDPs via Model-Free Reinforcement Learning , 2020, 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS).
[29] Sebastian Thrun,et al. Probabilistic robotics , 2002, CACM.
[30] Yishay Mansour,et al. Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.
[31] Daniel Kroening,et al. Modular Deep Reinforcement Learning with Temporal Logic Specifications , 2019, ArXiv.