# Towards a Domain-Specific Modelling Environment for Reinforcement Learning

arXiv: https://arxiv.org/abs/2410.09368 · HTML: https://mlsanity.com/paper/2410.09368/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 12 Oct 2024.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 4 of 4)

- [An Introduction to Deep Reinforcement Learning](https://mlsanity.com/paper/1811.12560/) (2018)
- [From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)](https://mlsanity.com/paper/2009.10632/) (2020)
- [Model-driven Engineering for Machine Learning Components: A Systematic Literature Review](https://mlsanity.com/paper/2311.00284/) (2023)
- [DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning](https://mlsanity.com/paper/1701.02284/) (2017)

## Built on by (most cited first, 1 of 1)

- [A Model-Driven Approach for Developing Families of Reinforcement Learning Environments](https://mlsanity.com/paper/2606.20324/) (2026)

## Abstract (arXiv)

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.