A Model-Driven Approach for Developing Families of Reinforcement Learning Environments

2026-06-19 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

Xiaoran Liu and Istvan David propose a model-driven approach to automate the development of families of Reinforcement Learning (RL) training environments, addressing the current labor-intensive and error-prone manual process. These virtual environments are crucial for training RL agents in realistic problems, often requiring multiple, slightly varied settings for convergence. Their method employs a hybrid genetic algorithm, combining population-based global search with heuristic local search, to generate these environment families. The approach expresses mutations and constraints as model transformations, which are then operationalized by a model transformation engine. The authors demonstrate the soundness of their technique in a wildfire mitigation scenario and for curriculum learning, a paradigm that specifically benefits from diverse environment families. This work, submitted on 18 Jun 2026 (arXiv:2606.20324), aims to enhance scalability and efficiency in RL environment creation.

Key takeaway

For Machine Learning Engineers developing complex Reinforcement Learning agents, you should consider adopting model-driven environment generation. This approach significantly reduces the manual effort and errors associated with creating diverse training environment families. By employing a hybrid genetic algorithm and model transformations, you can efficiently scale your environment development. This accelerates agent training and improves convergence, particularly for curriculum learning or scenarios like wildfire mitigation.

Key insights

A model-driven approach using a hybrid genetic algorithm automates the creation of diverse reinforcement learning environment families.

Principles

• Environment families are critical for RL convergence.
• Model transformations can define environment variations.
• Genetic algorithms can automate environment generation.

Method

A hybrid genetic algorithm, combining global and local search, generates RL environment families. Mutations and constraints are defined as model transformations, executed by a model transformation engine.

In practice

• Apply to wildfire mitigation scenarios.
• Use for curriculum learning paradigms.

Topics

• Reinforcement Learning
• Environment Generation
• Model-Driven Engineering
• Genetic Algorithms
• Curriculum Learning
• Software Engineering

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.