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Beyond the Cumulative Return in Reinforcement Learning
Abstract: Reinforcement Learning (RL) is a form of stochastic adaptive control in which one seeks to estimate parameters of a controller only from data, and has gained popularity in recent years. However, technological successes of RL are hindered by the high variance and irreproducibility their training exhibits in practice.
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