To enhance the interpretability of Reinforcement Learning (RL), we propose Revealing Evolutionary Action Consequence Trajectories (REACT). In contrast to the prevalent practice of validating RL models based on their optimal behavior learned during training, we posit that considering a range of edge-case trajectories provides a more comprehensive understanding of their inherent behavior. To induce such scenarios, we introduce a disturbance to the initial state, optimizing it through an evolutionary algorithm to generate a diverse population of demonstrations. To evaluate the fitness of trajectories, REACT incorporates a joint fitness function that encourages both local and global diversity in the encountered states and chosen actions. Through assessments with policies trained for varying durations in discrete and continuous environments, we demonstrate the descriptive power of REACT. Our results highlight its effectiveness in revealing nuanced aspects of RL models' behavior beyond optimal performance, thereby contributing to improved interpretability.
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React.js(React)是 Facebook 推出的一个用来构建用户界面的 JavaScript 库。
Facebook开源了React,这是该公司用于构建反应式图形界面的JavaScript库,已经应用于构建Instagram网站及 Facebook部分网站。最近出现了AngularJS、MeteorJS 和Polymer中实现的Model-Driven Views等框架,React也顺应了这种趋势。React基于在数据模型之上声明式指定用户界面的理念,用户界面会自动与底层数据保持同步。与前面提及 的框架不同,出于灵活性考虑,React使用JavaScript来构建用户界面,没有选择HTML。Not Rest