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Learning Efficient Abstract Planning Models that Choose What to Predict.

, , , , , and . CoRL, volume 229 of Proceedings of Machine Learning Research, page 2070-2095. PMLR, (2023)

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Associative Reinforcement Learning: Functions in k-DNF.. Mach. Learn., 15 (3): 279-298 (1994)FFRob: Leveraging symbolic planning for efficient task and motion planning., , and . Int. J. Robotics Res., 37 (1): 104-136 (2018)Meta-learning curiosity algorithms., , , and . ICLR, OpenReview.net, (2020)Input Generlization in Delayed Reinforcement Learning: An Algorithm and Performance Comparisons, and . Proc.\ 12th International Joint Conf.\ on Artificial Intelligence (IJCAI-91), Sydney, Australia, (1991)Learning Probabilistic Relational Dynamics for Multiple Tasks, , , and . CoRR, (2012)Segmentation According to Natural Examples: Learning Static Segmentation from Motion Segmentation., and . IEEE Trans. Pattern Anal. Mach. Intell., 31 (4): 661-676 (2009)Intelligent Interaction with the Real World.. ECML/PKDD (1), volume 6321 of Lecture Notes in Computer Science, page 3. Springer, (2010)Modeling and Planning with Macro-Actions in Decentralized POMDPs., , , and . J. Artif. Intell. Res., (2019)Partially Observable Markov Decision Processes for Artificial Intelligence., , and . KI, volume 981 of Lecture Notes in Computer Science, page 1-17. Springer, (1995)Object-Based World Modeling in Semi-Static Environments with Dependent Dirichlet Process Mixtures., , , and . IJCAI, page 3513-3521. IJCAI/AAAI Press, (2016)