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Analysis of adaptability of Reinforcement Learning approach

机译:强化学习方法的适应性分析

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Reinforcement Learning is a powerful tool which is being used for solving many optimization problems including power system scheduling problems. Even though there are theoretical results which suggest that under specified technical conditions, RL algorithms are adaptive, however, for power system scheduling problems the potential of adaptability is not still explored. In this paper, we explore, through simulation studies, the adaptability of an RL algorithm considering a simple multi stage decision making problem.
机译:强化学习是一种功能强大的工具,可用于解决许多优化问题,包括电力系统调度问题。尽管有理论结果表明,在特定的技术条件下,RL算法是自适应的,但是,对于电力系统调度问题,仍未探索适应性的潜力。在本文中,我们通过仿真研究探索了考虑简单的多阶段决策问题的RL算法的适应性。

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