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Lebesgue-Sampling-Based Optimal Control Problems With Time Aggregation
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DOI：10.1109/TAC.2010.2073610.png)
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摘要
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We formulate the Lebesgue-sampling-based optimal control problem. We show that the problem can be solved by the time aggregation approach in Markov decision processes (MDP) theory. Policy-iteration-based and reinforcement-learning-based methods are developed for the optimal policies. Both analytical solutions and sample-path-based algorithms are given. Compared to the periodic-sampling scheme, the Lebesgue sampling scheme improves system performance.
Keyword:
Aggregation
Markov decision processes (MDPs)
performance potentials
reinforcement learning
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期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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