论文标题
审查指标以衡量增强学习的稳定性，鲁棒性和弹性
Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning
论文作者
论文摘要
近年来，强化学习引起了极大的兴趣，这主要是由于深入强化学习在解决许多具有挑战性的任务（例如下棋，GO和在线计算机游戏）方面取得了成功。但是，随着对强化学习的越来越重视，游戏和模拟环境之外的应用需要了解强化学习方法的稳健性，稳定性和弹性。为此，我们进行了全面的文献综述，以对这三种行为的可用文献进行特征，因为它们与加强学习有关。我们对用于指示或测量鲁棒性，稳定性和弹性行为的定量和理论方法进行了分类。此外，我们确定了定量方法试图稳定，健壮或弹性的动作或事件。最后，我们提供了一个决策树，可用于选择指标来量化行为。我们认为，这是针对强化学习的稳定性，鲁棒性和韧性的首次全面综述。
Reinforcement learning has received significant interest in recent years, due primarily to the successes of deep reinforcement learning at solving many challenging tasks such as playing Chess, Go and online computer games. However, with the increasing focus on reinforcement learning, applications outside of gaming and simulated environments require understanding the robustness, stability, and resilience of reinforcement learning methods. To this end, we conducted a comprehensive literature review to characterize the available literature on these three behaviors as they pertain to reinforcement learning. We classify the quantitative and theoretical approaches used to indicate or measure robustness, stability, and resilience behaviors. In addition, we identified the action or event to which the quantitative approaches were attempting to be stable, robust, or resilient. Finally, we provide a decision tree useful for selecting metrics to quantify the behaviors. We believe that this is the first comprehensive review of stability, robustness and resilience specifically geared towards reinforcement learning.
