A Model-Driven Approach for Developing Families of Reinforcement Learning Environments
Researchers have developed a model-driven approach to streamline the creation of reinforcement learning (RL) environment families. This method uses a hybrid genetic algorithm to generate variations of training environments, addressing the labor-intensive and error-prone nature of manual development. The approach operationalizes mutations and constraints as model transformations, managed by a model transformation engine, and has been demonstrated in scenarios like wildfire mitigation and curriculum learning. AI
IMPACT Streamlines the creation of diverse training environments, potentially accelerating RL agent development and application.