Bootstrapping simulation-based algorithms with a suboptimal policy
Finding optimal policies for Markov Decision Processes with large state spaces is in general intractable. Nonetheless, simulation-based algorithms inspired by Sparse Sampling (SS) such as Upper Confidence Bound applied in Trees (UCT) and Forward Search Sparse Sampling (FSSS) have been shown to perfo...
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| المؤلفون الرئيسيون: | , , , |
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| التنسيق: | text |
| اللغة: | English |
| منشور في: |
Institutional Knowledge at Singapore Management University
2014
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://ink.library.smu.edu.sg/sis_research/3000 https://ink.library.smu.edu.sg/context/sis_research/article/4000/viewcontent/7934_37003_2_PB.pdf |
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