Performance evaluations are critical for quantifying algorithmic advances in reinforcement learning. Recent reproducibility analyses have shown that reported performance results are often inconsistent and difficult to replicate. In this work, we argue that the inconsistency of performance stems from the use of flawed evaluation metrics. Taking a step towards ensuring that reported results are consistent, we propose a new comprehensive evaluation methodology for reinforcement learning algorithms that produces reliable measurements of performance both on a single environment and when aggregated across environments. We demonstrate this method by evaluating a broad class of reinforcement learning algorithms on standard benchmark tasks.

中文翻译：

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评估强化学习算法的性能

性能评估对于量化强化学习中的算法进步至关重要。最近的重现性分析表明，报告的性能结果通常不一致且难以复制。在这项工作中，我们认为性能的不一致源于使用有缺陷的评估指标。为了确保报告的结果一致，我们为强化学习算法提出了一种新的综合评估方法，该方法可以在单个环境和跨环境聚合时产生可靠的性能测量。我们通过在标准基准任务上评估广泛的强化学习算法来演示这种方法。