Towards Reinforcement Learning for In-Place Model Transformations
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ACM / IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS), October 10-15, 2021, virtual event.
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.