为强化学习设计专用建模工具，降低使用门槛。
Towards a Domain-Specific Modelling Environment for Reinforcement Learning
- 用模型驱动工程构建强化学习专用建模语言RLML。
- 支持语法编辑、约束检查和代码自动生成。
- 适合无机器学习背景的用户快速上手强化学习。
近年来，机器学习技术广受欢迎，但其算法复杂性使非专业人士难以理解和应用。本文采用模型驱动工程（MDE）方法，针对强化学习领域开发了一种专用建模环境。提出的强化学习建模语言（RLML）支持语法导向编辑、约束检查及代码自动生成功能，并可比较多种强化学习算法的结果。实验验证了该方法在抽象强化学习技术、降低学习曲线方面的有效性，有助于提升非专业用户的使用体验。
原文摘要 · Abstract (English)
In recent years, machine learning technologies have gained immense popularity and are being used in a wide range of domains. However, due to the complexity associated with machine learning algorithms, it is a challenge to make it user-friendly, easy to understand and apply. Machine learning applications are especially challenging for users who do not have proficiency in this area. In this paper, we use model-driven engineering (MDE) methods and tools for developing a domain-specific modelling environment to contribute towards providing a solution for this problem. We targeted reinforcement learning from the machine learning domain, and evaluated the proposed language, reinforcement learning modelling language (RLML), with multiple applications. The tool supports syntax-directed editing, constraint checking, and automatic generation of code from RLML models. The environment also provides support for comparing results generated with multiple RL algorithms. With our proposed MDE approach, we were able to help in abstracting reinforcement learning technologies and improve the learning curve for RL users.
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