摘要：

Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past. A common approach is to use importance sampling techniques for compensating for the bias caused by the difference between data-collecting policies and the target policy. However, existing off-policy methods do not often take the variance of value function estimators explicitly into account and therefore their performance tends to be unstable. To cope with this problem, we propose using an adaptive importance sampling technique which allows us to actively control the trade-off between bias and variance. We further provide a method for optimally determining the trade-off parameter based on a statistical machine learning theory.

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