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We study strategy synthesis for partially observable Markov decision processes (POMDPs).
The temporal logic of programs
Amir Pnueli · 1977
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The complexity of Markov decision processes
Christos H. Papadimitriou and John N. Tsitsiklis · 1987
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L. Littman, and Anthony R. Cassandra · 1998
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On the undecidability of probabilistic planning and infinite-horizon partially observable Markov decision problems
Omid Madani, Steve Hanks, and Anne Condon · 1999
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Learning finite-state controllers for partially observable environments
Nicolas Meuleau, Leonid Peshkin, Kee-Eung Kim, and Leslie Pack Kaelbling · 1999
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Value-function approximations for partially observable Markov decision processes
Milos Hauskrecht · 2000
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Approximate planning in large POMDPs via reusable trajectories
Michael J Kearns, Yishay Mansour, and Andrew Y Ng · 2000
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Policy gradient methods for reinforcement learning with function approximation
Richard S. Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour · 2000
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Point-based value iteration: An anytime algorithm for POMDPs
Joelle Pineau, Geoff Gordon, and Sebastian Thrun · 2003
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Solving deep memory POMDPs with recurrent policy gradients
Daan Wierstra, Alexander Förster, Jan Peters, and Jürgen Schmidhuber · 2007
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Principles of Model Checking
Christel Baier and Joost-Pieter Katoen · 2008
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Optimizing fixed-size stochastic controllers for POMDPs and decentralized POMDPs
Christopher Amato, Daniel S Bernstein, and Shlomo Zilberstein · 2010
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Monte-carlo planning in large POMDPs
David Silver and Joel Veness · 2010
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On the computational complexity of stochastic controller optimization in POMDPs
Nikos Vlassis, Michael L. Littman, and David Barber · 2012
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How to construct deep recurrent neural networks
Razvan Pascanu, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio · 2013
Deep recurrent q-learning for partially observable MDPs
Matthew Hausknecht and Peter Stone · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, et al · 2015
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What is decidable about partially observable Markov decision processes with ω \omega -regular objectives
Krishnendu Chatterjee, Martin Chmelík, and Mathieu Tracol · 2016
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A storm is coming: A modern probabilistic model checker
Christian Dehnert, Sebastian Junges, Joost-Pieter Katoen, and Matthias Volk · 2017
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Reluplex: An efficient SMT solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer · 2017
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Minimal counterexamples for linear-time probabilistic verification
Ralf Wimmer, Nils Jansen, Erika Ábrahám, Joost-Pieter Katoen, and Bernd Becker · 2014
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Qualitative analysis of POMDPs with temporal logic specifications for robotics applications
Krishnendu Chatterjee, Martin Chmelík, Raghav Gupta, and Ayush Kanodia · 2015
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Keras, 2015
François Chollet · 2015
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Michael L. Littman, Ufuk Topcu, Jie Fu, Charles Isbell, Min Wen, and James MacGlashan · 2017
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Verification and control of partially observable probabilistic systems
Gethin Norman, David Parker, and Xueyi Zou · 2017
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Accelerated vector pruning for optimal POMDP solvers
Erwin Walraven and Matthijs Spaan · 2017
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Finite-state controllers of POMDPs via parameter synthesis
Sebastian Junges, Nils Jansen, Ralf Wimmer, Tim Quatmann, Leonore Winterer, Joost-Pieter Katoen, and Bernd Becker · 2018
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