Fetching the paper…
Reading the bibliography…
Recurrent neural networks (RNNs) have emerged as an effective representation of control policies in sequential decision-making problems.
Gedanken-experiments on sequential machines
Edward F Moore · 1956
Earlier work this paper cites.
The temporal logic of programs
Amir Pnueli · 1977
Earlier work this paper cites.
Supervised learning of probability distributions by neural networks
Eric B. Baum and Frank Wilczek · 1987
Earlier work this paper cites.
Learning finite machines with self-clustering recurrent networks
Zheng Zeng, Rodney M. Goodman, and Padhraic Smyth · 1993
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Simplified LQG control with neural networks
Ole Sørensen · 1997
Earlier work this paper cites.
Discrete time recurrent neural network architectures: A unifying review
Ah Chung Tsoi and Andrew D. Back · 1997
Earlier work this paper cites.
A survey of POMDP applications
Anthony R Cassandra · 1998
Earlier work this paper cites.
Solving POMDPs by searching the space of finite policies
Nicolas Meuleau, Kee-Eung Kim, Leslie Pack Kaelbling, and Anthony R. Cassandra · 1999
Earlier work this paper cites.
Reinforcement learning with long short-term memory
Bram Bakker · 2001
Earlier work this paper cites.
Bounded finite state controllers
Pascal Poupart and Craig Boutilier · 2003
Earlier work this paper cites.
Solving deep memory POMDPs with recurrent policy gradients
Daan Wierstra, Alexander Förster, Jan Peters, and Jürgen Schmidhuber · 2007
Earlier work this paper cites.
Principles of Model Checking
Christel Baier and Joost-Pieter Katoen · 2008
Cited alongside, same era.
Elements of information theory
Thomas M Cover and Joy A Thomas · 2012
Cited alongside, same era.
Maximizing entropy over Markov processes
Fabrizio Biondi, Axel Legay, Bo Friis Nielsen, and Andrzej Wasowski · 2013
Cited alongside, same era.
How to construct deep recurrent neural networks
Razvan Pascanu, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio · 2014
Cited alongside, same era.
Qualitatively characterizing neural network optimization problems
Ian J. Goodfellow and Oriol Vinyals · 2015
Cited alongside, same era.
Deep recurrent Q-learning for partially observable MDPs
Matthew J. Hausknecht and Peter Stone · 2015
Cited alongside, same era.
Reluplex: An efficient SMT solver for verifying deep neural networks
Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer · 2017
Later among the works it cites.
Verification and control of partially observable probabilistic systems
Gethin Norman, David Parker, and Xueyi Zou · 2017
Later among the works it cites.
Accelerated vector pruning for optimal pomdp solvers
Erwin Walraven and Matthijs T. J. Spaan · 2017
Later among the works it cites.
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
Later among the works it cites.
Verification of recurrent neural networks through rule extraction
Qinglong Wang, Kaixuan Zhang, Xue Liu, and C. Lee Giles · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Memory-based control with recurrent neural networks
Nicolas Heess, Jonathan J. Hunt, Timothy P. Lillicrap, and David Silver · 2015
Cited alongside, same era.
A survey on the application of recurrent neural networks to statistical language modeling
Wim De Mulder, Steven Bethard, and Marie-Francine Moens · 2015
Cited alongside, same era.
A symbolic SAT-based algorithm for almost-sure reachability with small strategies in POMDPs
Krishnendu Chatterjee, Martin Chmelik, and Jessica Davies · 2016
Cited alongside, same era.
Deep learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
Cited alongside, same era.
Maximum entropy methods for extracting the learned features of deep neural networks
Alex Finnegan and Jun S Song · 2017
Cited alongside, same era.
Safety verification of deep neural networks
Xiaowei Huang, Marta Kwiatkowska, Sen Wang, and Min Wu · 2017
Cited alongside, same era.
Extracting automata from recurrent neural networks using queries and counterexamples
Gail Weiss, Yoav Goldberg, and Eran Yahav · 2018
Later among the works it cites.
Verification of RNN-based neural agent-environment systems
Michael E. Akintunde, Andreea Kevorchian, Alessio Lomuscio, and Edoardo Pirovano · 2019
Later among the works it cites.
Counterexample-guided strategy improvement for POMDPs using recurrent neural networks
Steven Carr, Nils Jansen, Ralf Wimmer, Alexandru Constantin Serban, Bernd Becker, and Ufuk Topcu · 2019
Later among the works it cites.
Learning finite state representations of recurrent policy networks
Anurag Koul, Alan Fern, and Sam Greydanus · 2019
Later among the works it cites.
Representing formal languages: A comparison between finite automata and recurrent neural networks
Joshua J. Michalenko, Ameesh Shah, Abhinav Verma, Richard G. Baraniuk, Swarat Chaudhuri, and Ankit B. Patel · 2019
Later among the works it cites.
Interpreting and evaluating neural network robustness
Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, and Xiang Chen · 2019
Later among the works it cites.