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Efficiently solving problems with large action spaces using A* search remains a significant challenge.
Dynamic Programming
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Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell Stinchcombe, and Halbert White · 1989
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Discovering admissible search heuristics by abstracting and optimizing
Jack Mostow and Armand Prieditis · 1989
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Stuart Russell · 1992
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Christopher JCH Watkins and Peter Dayan · 1992
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Anna Bramanti-Gregor and Henry W. Davis · 1993
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Reinforcement learning: An introduction , volume 1
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A* with partial expansion for large branching factor problems
Takayuki Yoshizumi, Teruhisa Miura, and Toru Ishida · 2000
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Planning as heuristic search
Blai Bonet and Héctor Geffner · 2001
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The ff planning system: Fast plan generation through heuristic search
Jörg Hoffmann and Bernhard Nebel · 2001
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Marco Ernandes and Marco Gori · 2004
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The Fast Downward planning system
Malte Helmert · 2006
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Online learning of search heuristics
Michael Fink · 2007
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Compressing pattern databases with learning
Mehdi Samadi, Maryam Siabani, Ariel Felner, and Robert Holte · 2008
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Preferred operators and deferred evaluation in satisficing planning
Silvia Richter and Malte Helmert · 2009
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Using neural networks for evaluation in heuristic search algorithm
Hung-Che Chen and Jyh-Da Wei · 2011
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Partial-expansion a* with selective node generation
Ariel Felner, Meir Goldenberg, Guni Sharon, Roni Stern, Tal Beja, Nathan Sturtevant, Jonathan Schaeffer, and Robert C. Holte · 2012
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Optimal-generation variants of EPEA
Meir Goldenberg, Ariel Felner, Nathan R. Sturtevant, Robert C. Holte, and Jonathan Schaeffer · 2013
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Solving the rubik’s cube with approximate policy iteration
Stephen McAleer, Forest Agostinelli, Alexander Shmakov, and Pierre Baldi · 2019
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Learning heuristic functions for mobile robot path planning using deep neural networks
Takeshi Takahashi, He Sun, Dong Tian, and Yebin Wang · 2019
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DeepcubeA
Forest Agostinelli, Stephen McAleer, Alexander Shmakov, and Pierre Baldi · 2020
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Retro*: Learning retrosynthetic planning with neural guided a* search
Binghong Chen, Chengtao Li, Hanjun Dai, and Le Song · 2020
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Neural network heuristics for classical planning: A study of hyperparameter space
Patrick Ferber, Malte Helmert, and Jörg Hoffmann · 2020
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3D cube algorithm for the key generation method: Applying deep neural network learning-based
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Toward rational deployment of multiple heuristics in A
David Tolpin, Tal Beja, Solomon Eyal Shimony, Ariel Felner, and Erez Karpas · 2013
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
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Learning to rank for synthesizing planning heuristics
Caelan Reed Garrett, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning domain-independent planning heuristics with hypergraph networks
William Shen, Felipe W. Trevizan, and Sylvie Thiébaux · 2020
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Asnets: Deep learning for generalised planning
Sam Toyer, Sylvie Thiébaux, Felipe W. Trevizan, and Lexing Xie · 2020
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Topological quantum compiling with reinforcement learning
Yuan-Hang Zhang, Pei-Lin Zheng, Yi Zhang, and Dong-Ling Deng · 2020
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Obtaining approximately admissible heuristic functions through deep reinforcement learning and A* search
Forest Agostinelli, Stephen McAleer, Alexander Shmakov, Roy Fox, Marco Valtorta, Biplav Srivastava, and Pierre Baldi · 2021
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Leah Chrestien, Tomás Pevný, Antonín Komenda, and Stefan Edelkamp · 2021
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Policy-guided heuristic search with guarantees
Laurent Orseau and Levi H. S. Lelis · 2021
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Puzzle-based parking
Parag J Siddique, Kevin R Gue, and John S Usher · 2021
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Model-based visual planning with self-supervised functional distances
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Optimal search with neural networks: Challenges and approaches
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Optimize planning heuristics to rank, not to estimate cost-to-goal
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On solving the rubik’s cube with domain-independent planners using standard representations
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Twisty-puzzle-inspired approach to clifford synthesis
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Learning heuristic functions for large state spaces
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