The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games. It supports a variety of different problem settings and it has been receiving increasing attention from the scientific community, leading to some high-profile success stories such as the much publicized Deep Q-Networks (DQN). In this article we take a big picture look at how the ALE is being used by the research community. We show how diverse the evaluation methodologies in the ALE have become with time, and highlight some key concerns when evaluating agents in the ALE. We use this discussion to present some methodological best practices and provide new benchmark results using these best practices. To further the progress in the field, we introduce a new version of the ALE that supports multiple game modes and provides a form of stochasticity we call sticky actions. We conclude this big picture look by revisiting challenges posed when the ALE was introduced, summarizing the state-of-the-art in various problems and highlighting problems that remain open.

该文章探讨了 Arcade Learning Environment（ALE） 作为评估平台对研究社区的影响，并重点说明在该平台中评估代理时的一些核心问题。此外，该文章还提出了一些研究方法的最佳实践，并介绍了一个支持多种游戏模式的新版本 ALE，提供了一种被称为粘性操作的随机性。

重新审视街机学习环境：通用智能体的评估协议和开放问题