Corpus ID: 218665668

Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning

@article{Moerland2020ThinkTF,
  title={Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning},
  author={T. M. Moerland and Anna Deichler and S. Baldi and J. Broekens and Catholijn M. Jonker},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.07404}
}
  • T. M. Moerland, Anna Deichler, +2 authors Catholijn M. Jonker
  • Published 2020
  • Computer Science
  • ArXiv
  • Planning and reinforcement learning are two key approaches to sequential decision making. Multi-step approximate real-time dynamic programming, a recently successful algorithm class of which AlphaZero [Silver et al., 2018] is an example, combines both by nesting planning within a learning loop. However, the combination of planning and learning introduces a new question: how should we balance time spend on planning, learning and acting? The importance of this trade-off has not been explicitly… CONTINUE READING
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    CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
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