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- W4385517529 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." @default.
- W4385517529 created "2023-08-04" @default.
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- W4385517529 date "2023-01-01" @default.
- W4385517529 modified "2023-10-18" @default.
- W4385517529 title "Model-Based Reinforcement Learning with State Abstraction: A Survey" @default.
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- W4385517529 doi "https://doi.org/10.1007/978-3-031-39144-6_9" @default.
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