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Stochastic learning and optimization : a sensitivity-based approach
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| 目次 | Introduction Perturbation analysis Learning and optimization with perturbation analysis Markov decision processes Sample-path-based policy iteration Reinforcement learning Adaptive control problems as MDPs Event-based optimization of Markov systems Constructing sensitivity formulas Probablility and Markov processes Stochastic matrices Queueing theory.続きを見る |
| 冊子版へのリンク | http://hdl.handle.net/2324/1001228346 |
| 本文を見る | Full text available from Springer Computer Science eBooks 2007 English/International |
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| 登録日 | 2020.06.27 |
| 更新日 | 2020.06.28 |