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Non-markovian Reinforcement Learning (RL) tasks are very hard to solve, because agents must consider the entire history of state-action pairs to act rationally in the environment.
The temporal logic of programs
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Linear temporal logic and linear dynamic logic on finite traces
G. De Giacomo and M. Y. Vardi · 2013
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Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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Categorical reparameterization with gumbel-softmax
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Environment-independent task specifications via GLTL
M. L. Littman, U. Topcu, J. Fu, C. L. I. Jr., M. Wen, and J. MacGlashan · 2017
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Recurrent world models facilitate policy evolution
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Deepproblog: Neural probabilistic logic programming
R. Manhaeve, S. Dumancic, A. Kimmig, T. Demeester, and L. De Raedt · 2018
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R. S. Sutton and A. G. Barto · 2018
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A semantic loss function for deep learning with symbolic knowledge
J. Xu, Z. Zhang, T. Friedman, Y. Liang, and G. Van den Broeck · 2018
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Ltl and beyond: Formal languages for reward function specification in reinforcement learning
A. Camacho, R. Toro Icarte, T. Q. Klassen, R. Valenzano, and S. A. McIlraith · 2019
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Bridging machine learning and logical reasoning by abductive learning
W.-Z. Dai, Q. Xu, Y. Yu, and Z.-H. Zhou · 2019
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Foundations for restraining bolts: Reinforcement learning with ltlf/ldlf restraining specifications, 2019
G. D. Giacomo, L. Iocchi, M. Favorito, and F. Patrizi · 2019
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Recurrent experience replay in distributed reinforcement learning
S. Kapturowski, G. Ostrovski, W. Dabney, J. Quan, and R. Munos · 2019
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Specification patterns for robotic missions
C. Menghi, C. Tsigkanos, P. Pelliccione, C. Ghezzi, and T. Berger · 2019
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Reinforcement learning based temporal logic control with maximum probabilistic satisfaction
M. Cai, S. Xiao, B. Li, Z. Li, and Z. Kan · 2020
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M. Gaon and R. Brafman · 2020
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Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas
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P. Vaezipoor, A. C. Li, R. T. Icarte, and S. A. McIlraith · 2021
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Learning finite linear temporal logic specifications with a specialized neural operator, 2021
H. Walke, D. Ritter, C. Trimbach, and M. Littman · 2021
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Active finite reward automaton inference and reinforcement learning using queries and counterexamples
Z. Xu, B. Wu, A. Ojha, D. Neider, and U. Topcu · 2021
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Reinforcement learning with stochastic reward machines
J. Corazza, I. Gavran, and D. Neider · 2022
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Reward machines: Exploiting reward function structure in reinforcement learning
R. T. Icarte, T. Q. Klassen, R. A. Valenzano, and S. A. McIlraith · 2022
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Y. Kuo, B. Katz, and A. Barbu · 2020
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Logic tensor networks
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Modular deep reinforcement learning for continuous motion planning with temporal logic
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Foundations for restraining bolts: Reinforcement learning with ltlf/ldlf restraining specifications
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Markov abstractions for PAC reinforcement learning in non-markov decision processes
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Joint learning of reward machines and policies in environments with partially known semantics
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Not all neuro-symbolic concepts are created equal: Analysis and mitigation of reasoning shortcuts, 2023
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Grounding LTLf Specifications in Image Sequences
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