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Meta-planning, or learning to guide planning from experience, is a promising approach to improving the computational cost of planning.
Aggregation in dynamic programming
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C. Boutilier · 1997
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PDDL-the planning domain definition language, 1998
D. McDermott, M. Ghallab, A. Howe, C. Knoblock, A. Ram, M. Veloso, D. Weld, and D. Wilkins · 1998
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On the role of context-specific independence in probabilistic inference
N. L. Zhang and D. Poole · 1999
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Spudd: Stochastic planning using decision diagrams
J. Hoey, R. St-Aubin, A. J. Hu, and C. Boutilier · 1999
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Decision-theoretic planning: Structural assumptions and computational leverage
C. Boutilier, T. Dean, and S. Hanks · 1999
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Rrt-connect: An efficient approach to single-query path planning
J. J. Kuffner and S. M. LaValle · 2000
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FF: The fast-forward planning system
J. Hoffmann · 2001
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Efficient solution algorithms for factored mdps
C. Guestrin, D. Koller, R. Parr, and S. Venkataraman · 2003
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Efficient probabilistic reasoning in bns with mutual exclusion and context-specific independence
C. Domshlak and S. E. Shimony · 2004
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Navigation among movable obstacles: Real-time reasoning in complex environments
M. Stilman and J. J. Kuffner · 2005
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State abstraction discovery from irrelevant state variables
N. K. Jong and P. Stone · 2005
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Solving large stochastic planning problems using multiple dynamic abstractions
K. A. Steinkraus · 2005
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Towards a unified theory of state abstraction for mdps
L. Li, T. J. Walsh, and M. L. Littman · 2006
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The fast downward planning system
M. Helmert · 2006
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Relational envelope-based planning
N. Hernandez-Gardiol · 2008
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Efficient skill learning using abstraction selection
G. Konidaris and A. Barto · 2009
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A survey of monte carlo tree search methods
Mastering the game of Go with deep neural networks and tree search
D. Silver, A. Huang, C. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis · 2016
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Guided search for task and motion plans using learned heuristics
R. Chitnis, D. Hadfield-Menell, A. Gupta, S. Srivastava, E. Groshev, C. Lin, and P. Abbeel · 2016
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Constructing abstraction hierarchies using a skill-symbol loop
G. Konidaris · 2016
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Learning to guide task and motion planning using score-space representation
B. Kim, L. P. Kaelbling, and T. Lozano-Pérez · 2017
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Learning feasibility constraints for multi-contact locomotion of legged robots
J. Carpentier, R. Budhiraja, and N. Mansard · 2017
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C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton · 2012
Cited alongside, same era.
Proximity-based non-uniform abstractions for approximate planning
J. Baum, A. E. Nicholson, and T. I. Dix · 2012
Cited alongside, same era.
Context-specific approximation in probabilistic inference
D. L. Poole · 2013
Cited alongside, same era.
Combined task and motion planning through an extensible planner-independent interface layer
S. Srivastava, E. Fang, L. Riano, R. Chitnis, S. Russell, and P. Abbeel · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Abstraction selection in model-based reinforcement learning
N. Jiang, A. Kulesza, and S. Singh · 2015
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D. Abel, D. E. Hershkowitz, and M. L. Littman · 2017
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Learning feasibility for task and motion planning in tabletop environments
A. M. Wells, N. T. Dantam, A. Shrivastava, and L. E. Kavraki · 2018
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Guiding search in continuous state-action spaces by learning an action sampler from off-target search experience
B. Kim, L. P. Kaelbling, and T. Lozano-Pérez · 2018
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Value preserving state-action abstractions, 2019
D. Abel, N. Umbanhowar, K. Khetarpal, D. Arumugam, D. Precup, and M. L. Littman · 2019
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Learning value functions with relational state representations for guiding task-and-motion planning
B. Kim and L. Shimanuki · 2019
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Deep visual reasoning: Learning to predict action sequences for task and motion planning from an initial scene image
D. Driess, J.-S. Ha, and M. Toussaint · 2020
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Think too fast nor too slow: The computational trade-off between planning and reinforcement learning, 2020
T. M. Moerland, A. Deichler, S. Baldi, J. Broekens, and C. M. Jonker · 2020
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Deep visual heuristics: Learning feasibility of mixed-integer programs for manipulation planning
D. Driess, O. Oguz, J.-S. Ha, and M. Toussaint · 2020
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