论文标题
Vizarel：一种帮助更好地了解RL代理的系统
Vizarel: A System to Help Better Understand RL Agents
论文作者
论文摘要
监督学习的可视化工具使用户可以解释，内省并获得其模型成功和失败的直觉。强化学习从业人员提出许多相同的问题时，现有工具不适用于RL设置。在这项工作中，我们通过确定该系统应封装的可能功能来描述我们初步尝试构建这些想法原型的尝试。我们的设计是通过设想系统作为一个可以在可解释的强化学习中进行实验的平台来激发的。
Visualization tools for supervised learning have allowed users to interpret, introspect, and gain intuition for the successes and failures of their models. While reinforcement learning practitioners ask many of the same questions, existing tools are not applicable to the RL setting. In this work, we describe our initial attempt at constructing a prototype of these ideas, through identifying possible features that such a system should encapsulate. Our design is motivated by envisioning the system to be a platform on which to experiment with interpretable reinforcement learning.
