Sample-efficient iterative lower bound optimization of deep reactive policies for planning in continuous MDPs
Recent advances in deep learning have enabled optimization of deep reactive policies (DRPs) for continuous MDP planning by encoding a parametric policy as a deep neural network and exploiting automatic differentiation in an end-toend model-based gradient descent framework. This approach has proven e...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2022
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| Online Access: | https://ink.library.smu.edu.sg/sis_research/7724 https://ink.library.smu.edu.sg/context/sis_research/article/8727/viewcontent/21220_Article_Text_25233_1_2_20220628.pdf |
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| Institution: | Singapore Management University |
| Language: | English |