Publications• Sorted by Date • Classified by Publication Type • Classified by Research Category • Model-Based Reinforcement Learning with State Abstraction: A SurveyRolf A. N. Starre, Marco Loog, and Frans A. Oliehoek. Model-Based Reinforcement Learning with State Abstraction: A Survey. In Proceedings of the 34th Benelux Conference on Artificial Intelligence (BNAIC) and the 30th Belgian Dutch Conference on Machine Learning (Benelearn), November 2022. DownloadAbstractModel-based reinforcement learning methods are promising since they can increase sample efficiency while simultaneously improving generalizability. Learning can also be made more efficient through state abstraction, which delivers more compact models. Model-based reinforcement learning methods have been combined with learning abstract models to profit from both effects. We consider a wide range of state abstractions that have been covered in the literature, from straightforward state aggregation to deep learned representations, and sketch challenges that arise when combining model-based reinforcement learning with abstraction. We further show how various methods deal with these challenges and point to open questions and opportunities for further research. BibTeX Entry@inproceedings{Starre22BNAICBenelearn, author = {Starre, Rolf A. N. and Loog, Marco and Oliehoek, Frans A.}, title = {Model-Based Reinforcement Learning with State Abstraction: A Survey}, booktitle = BNAICBenelearn22, year = 2022, month = nov, keywords = {refereed}, abstract = { Model-based reinforcement learning methods are promising since they can increase sample efficiency while simultaneously improving generalizability. Learning can also be made more efficient through state abstraction, which delivers more compact models. Model-based reinforcement learning methods have been combined with learning abstract models to profit from both effects. We consider a wide range of state abstractions that have been covered in the literature, from straightforward state aggregation to deep learned representations, and sketch challenges that arise when combining model-based reinforcement learning with abstraction. We further show how various methods deal with these challenges and point to open questions and opportunities for further research. } }
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