Towards Reinforcement Learning for In-Place Model Transformations
Model-driven optimization has gained much interest in the last years which resulted in several dedicated extensions for in-place model transformation engines. The main idea is to exploit domain-specific languages to define models which are optimized by applying a set of model transformation rules. Objectives are guiding the optimization processes which are currently mostly realized by meta-heuristic searchers such as different kinds of Genetic Algorithms. However, meta-heuristic search approaches are currently challenged by reinforcement learning approaches for solving optimization problems.
In this new idea paper, we apply for the first time reinforcement learning for in-place model transformations. In particular, we extend an existing model-driven optimization approach with reinforcement learning techniques. We experiment with value-based and policy-based techniques. We run several case studies for validating the feasibility of using reinforcement learning for model-driven optimization and compare the performance against existing approaches. The initial evaluation shows promising results but also helped in identifying a dedicated research roadmap for the whole community.
Thu 14 OctDisplayed time zone: Osaka, Sapporo, Tokyo change
23:00 - 00:00
Machine learning and Recommender systems IITechnical Papers at Room 1
Chair(s): Antonio Cicchetti Mälardalen University
|Recommender Systems in Model-Driven Engineering: A Systematic Mapping ReviewJ1ST|
Lissette Almonte Universidad Autónoma de Madrid, Esther Guerra , Iván Cantador Universidad Autonoma de Madrid, Juan de Lara Autonomous University of Madrid
|A GNN-based Recommender System to Assist the Specification of Metamodels and ModelsFT|
Juri Di Rocco University of L'Aquila, Claudio Di Sipio University of L'Aquila, Davide Di Ruscio University of L'Aquila, Phuong T. Nguyen University of L’AquilaPre-print
|Towards Reinforcement Learning for In-Place Model TransformationsVISION|
Martin Eisenberg , Hans-Peter Pichler JKU Linz, Antonio Garmendia , Manuel Wimmer JKU Linz