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We present a reinforcement learning (RL) framework to synthesize a control policy from a given linear temporal logic (LTL) specification in an unknown stochastic environment that can be modeled as a Markov Decision Process (MDP).
Convergence of stochastic iterative dynamic programming algorithms
Tommi Jaakkola, Michael I. Jordan, and Satinder P. Singh · 1994
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Pac model-free reinforcement learning
Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, and Michael L. Littman · 2006
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Where’s Waldo? sensor-based temporal logic motion planning
H. Kress-Gazit, G. E. Fainekos, and G. J. Pappas · 2007
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Sertac Karaman, Matthew R Walter, Alejandro Perez, Emilio Frazzoli, and Seth Teller · 2011
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Optimal path planning for surveillance with temporal-logic constraints
Stephen L Smith, Jana Tůmová, Calin Belta, and Daniela Rus · 2011
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Formal approach to the deployment of distributed robotic teams
Y. Chen, X. C. Ding, A. Stefanescu, and C. Belta · 2012
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Temporal logic motion planning and control with probabilistic satisfaction guarantees
M. Lahijanian, S. B. Andersson, and C. Belta · 2012
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Robust control of uncertain Markov decision processes with temporal logic specifications
E. M. Wolff, U. Topcu, and R. M. Murray · 2012
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Sampling-based temporal logic path planning
C. I. Vasile and C. Belta · 2013
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Automated verification and strategy synthesis for probabilistic systems
Marta Kwiatkowska and David Parker · 2013
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Optimization-based trajectory generation with linear temporal logic specifications
E. M. Wolff, U. Topcu, and R. M. Murray · 2014
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Optimal control of Markov decision processes with linear temporal logic constraints
X. Ding, S. L. Smith, C. Belta, and D. Rus · 2014
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Probably approximately correct MDP learning and control with temporal logic constraints, 2014
Jie Fu and Ufuk Topcu · 2014
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Verification of Markov decision processes using learning algorithms
Tomáš Brázdil, Krishnendu Chatterjee, Martin Chmelík, Vojtěch Forejt, Jan Křetínský, Marta Kwiatkowska, David Parker, and Mateusz Ujma · 2014
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A learning based approach to control synthesis of Markov decision processes for linear temporal logic specifications
D. Sadigh, E. S. Kim, S. Coogan, S. S. Sastry, and S. A. Seshia · 2014
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Probabilistic motion planning under temporal tasks and soft constraints
M. Guo and M. M. Zavlanos · 2018
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Teaching multiple tasks to an RL agent using LTL
Rodrigo Toro Icarte, Toryn Q Klassen, Richard Valenzano, and Sheila A McIlraith · 2018
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A policy search method for temporal logic specified reinforcement learning tasks
Xiao Li, Yao Ma, and Calin Belta · 2018
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Logically-constrained reinforcement learning
Mohammadhosein Hasanbeig, Alessandro Abate, and Daniel Kroening · 2018
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Reinforcement Learning: An Introduction
Richard S Sutton and Andrew G Barto · 2018
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Rabinizer 4: From LTL to your favourite deterministic automaton
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Multi-agent plan reconfiguration under local LTL specifications
Meng Guo and Dimos V Dimarogonas · 2015
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Correct-by-synthesis reinforcement learning with temporal logic constraints
Min Wen, Rüdiger Ehlers, and Ufuk Topcu · 2015
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Lazy Probabilistic Model Checking without Determinisation
Ernst Moritz Hahn, Guangyuan Li, Sven Schewe, Andrea Turrini, and Lijun Zhang · 2015
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Q-learning for robust satisfaction of signal temporal logic specifications
D. Aksaray, A. Jones, Z. Kong, M. Schwager, and C. Belta · 2016
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Limit-deterministic Büchi automata for linear temporal logic
Salomon Sickert, Javier Esparza, Stefan Jaax, and Jan Křetínský · 2016
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Sampling-based control synthesis for multi-robot systems under global temporal specifications
Y. Kantaros and M. M. Zavlanos · 2017
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Reinforcement learning with temporal logic rewards
X. Li, C. Vasile, and C. Belta · 2017
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Sampling-based optimal control synthesis for multirobot systems under global temporal tasks
Y. Kantaros and M. M. Zavlanos · 2019
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Foundations for restraining bolts: Reinforcement learning with LTL f
Giuseppe De Giacomo, Luca Iocchi, Marco Favorito, and Fabio Patrizi · 2019
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Reduced variance deep reinforcement learning with temporal logic specifications
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Omega-regular objectives in model-free reinforcement learning
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CSRL, 2019
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