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Learning a well-informed heuristic function for hard task planning domains is an elusive problem.
Transformer Dissection: An Unified Understanding for Transformer’s Attention via the Lens of Kernel
Tsai, Y.-H. H.; Bai, S.; Yamada, M.; Morency, L.-P.; and Salakhutdinov, R. 2019 · 1908
Earlier work this paper cites.
A formal basis for the heuristic determination of minimum cost paths
Hart, P. E.; Nilsson, N. J.; and Raphael, B. 1968 · 1968
Earlier work this paper cites.
STRIPS: A new approach to the application of theorem proving to problem solving
Fikes, R. E.; and Nilsson, N. J. 1971 · 1971
Earlier work this paper cites.
Discovering Admissible Heuristics by Abstracting and Optimizing: A Transformational Approach
Mostow, J.; and Prieditis, A. 1989 · 1989
Earlier work this paper cites.
Alvinn: An autonomous land vehicle in a neural network
Pomerleau, D. A. 1989 · 1989
Earlier work this paper cites.
Acquiring Recursive Concepts with Explanation-Based Learning
Shavlik, J. W. 1989 · 1989
Earlier work this paper cites.
Learning and development in neural networks: the importance of starting small
Elman, J. L. 1993 · 1993
Earlier work this paper cites.
Learning to play the game of chess
Thrun, S. 1994 · 1994
Earlier work this paper cites.
Complexity Results for SAS+ Planning
Bäckström, C.; and Nebel, B. 1995 · 1995
Earlier work this paper cites.
Sokoban is PSPACE-complete
Culberson, J. 1999 · 1999
Earlier work this paper cites.
Is imitation learning the route to humanoid robots?
Schaal, S. 1999 · 1999
Earlier work this paper cites.
Automatic Synthesis and Use of Generic Types in Planning
Long, D.; and Fox, M. 2000 · 2000
Earlier work this paper cites.
Planning as heuristic search
Bonet, B.; and Geffner, H. 2001 · 2001
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Programming backgammon using self-teaching neural nets
Tesauro, G. 2002 · 2002
Earlier work this paper cites.
PDDL2. 1: An extension to PDDL for expressing temporal planning domains
Fox, M.; and Long, D. 2003 · 2003
Cited alongside, same era.
Likely-admissible and sub-symbolic heuristics
Ernandes, M.; and Gori, M. 2004 · 2004
Cited alongside, same era.
VAL: Automatic Plan Validation, Continuous Effects and Mixed Initiative Planning Using PDDL
Howey, R.; Long, D.; and Fox, M. 2004 · 2004
Cited alongside, same era.
Skill acquisition via transfer learning and advice taking
Torrey, L.; Shavlik, J.; Walker, T.; and Maclin, R. 2006 · 2006
Cited alongside, same era.
The Tradeoffs of Large Scale Learning
Bottou, L.; and Bousquet, O. 2007 · 2007
Cited alongside, same era.
Curriculum learning
Bengio, Y.; Louradour, J.; Collobert, R.; and Weston, J. 2009 · 2009
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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SymBA*: A symbolic bidirectional A* planner
Torralba, A.; Alcázar, V.; Borrajo, D.; Kissmann, P.; and Edelkamp, S. 2014 · 2014
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Classical planning in deep latent space: Bridging the subsymbolic-symbolic boundary
Asai, M.; and Fukunaga, A. 2017 · 2017
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Learning generalized reactive policies using deep neural networks
Groshev, E.; Goldstein, M.; Tamar, A.; Srivastava, S.; and Abbeel, P. 2017 · 2017
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The LAMA planner: Guiding cost-based anytime planning with landmarks
Richter, S.; and Westphal, M. 2010 · 2010
Cited alongside, same era.
The first learning track of the international planning competition
Fern, A.; Khardon, R.; and Tadepalli, P. 2011 · 2011
Cited alongside, same era.
A new representation and associated algorithms for generalized planning
Srivastava, S.; Immerman, N.; and Zilberstein, S. 2011 · 2011
Cited alongside, same era.
Heuristic Search - Theory and Applications
Edelkamp, S.; and Schrödl, S. 2012 · 2012
Cited alongside, same era.
Inductive policy selection for first-order MDPs
Yoon, S. W.; Fern, A.; and Givan, R. 2012 · 2012
Cited alongside, same era.
Learning heuristic functions for cost-based planning
Virseda, J.; Borrajo, D.; and Alcázar, V. 2013 · 2013
Cited alongside, same era.
Imagination-augmented agents for deep reinforcement learning
Racanière, S.; Weber, T.; Reichert, D.; Buesing, L.; Guez, A.; Jimenez Rezende, D.; Puigdomènech Badia, A.; Vinyals, O.; Heess, N.; Li, Y.; et al. 2017 · 2017
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Mastering the game of go without human knowledge
Silver, D.; Schrittwieser, J.; Simonyan, K.; Antonoglou, I.; Huang, A.; Guez, A.; Hubert, T.; Baker, L.; Lai, M.; Bolton, A.; et al. 2017 · 2017
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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gym-sokoban
Schrader, M.-P. B. 2018 · 2018
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Solving the Rubik’s cube with deep reinforcement learning and search
Agostinelli, F.; McAleer, S.; Shmakov, A.; and Baldi, P. 2019 · 2019
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Deep Learning for Cost-Optimal Planning: Task-Dependent Planner Selection
Sievers, S.; Katz, M.; Sohrabi, S.; Samulowitz, H.; and Ferber, P. 2019 · 2019
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Learning Neural Search Policies for Classical Planning
Gomoluch, P.; Alrajeh, D.; Russo, A.; and Bucchiarone, A. 2020 · 2020
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Learning heuristic functions for large state spaces
Arfaee, S. J.; Zilles, S.; and Holte, R. C. 2011 · 2098
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