mlsanityData to 14 Oct 2026

Towards a Domain-Specific Modelling Environment for Reinforcement Learning

arXiv 2410.09368 ↗First posted 12 Oct 2024cs.SE cs.AIcited by 1

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

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.

Key facts. Cited by 1 paper 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.

Builds on

4 of 23 references.

References · 4

  1. 2018An Introduction to Deep Reinforcement Learningcited by 206
  2. 2020From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)cited by 8
  3. 2023Model-driven Engineering for Machine Learning Components: A Systematic Literature Reviewcited by 7
  4. 2017DeepDSL: A Compilation-based Domain-Specific Language for Deep Learningcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026A Model-Driven Approach for Developing Families of Reinforcement Learning Environmentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents
  2. 2024Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods
  3. 2025Rulebook: bringing co-routines to reinforcement learning environments
  4. 2025A Technical Survey of Reinforcement Learning Techniques for Large Language Models
  5. 2022A Survey on Model-based Reinforcement Learning
  6. 2024Using Large Language Models to Automate and Expedite Reinforcement Learning with Reward Machine
  7. 2025Towards General-Purpose Model-Free Reinforcement Learning
  8. 2024A Research Agenda for Usability and Generalisation in Reinforcement Learning