Towards a Domain-Specific Modelling Environment for Reinforcement Learning
This work addresses the problem of making reinforcement learning more accessible for users without proficiency in machine learning, though it is incremental as it applies existing MDE methods to a new domain.
The paper tackles the complexity of machine learning algorithms by developing a domain-specific modeling environment for reinforcement learning using model-driven engineering, resulting in a tool that supports syntax editing, constraint checking, code generation, and algorithm comparison to abstract RL technologies and improve user learning curves.
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.