M .Eisenberg, H.P. Pichler, A. Garmendia, M. Wimmer: Towards Reinforcement Learning for In-Place Model Transformations, ACM / IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS), October 10-15, 2021. pdf


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 ideas 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 valuebased and policy-based techniques. We investigate 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 future research lines for the whole model transformation community.
paper

Towards Reinforcement Learning for In-Place Model Transformations