Feature-Conditioned Reinforcement Learning for Generalizable Engineering Optimization: Benchmarking on Multimodal Test Functions

Generalization is essential for accelerating opti- mization workflow, enhancing scalability and reducing compu- tational cost. This paper presents a novel Feature-Conditioned Reinforcement Learning (FC-RL) method for engineering opti- mization, which enables generalization across diverse problem scenarios by conditioning the RL policy on explicit problem features. Unlike traditional optimization methods that require hyperparameter tuning for each new task, FC-RL leverages a meta-network to condition its behavior dynamically through Feature-wise Linear Modulation (FiLM). The experiments conducted across six multi-modal benchmark functions with varying dimensions, search-space, and global optimum shifts demonstrate that FC-RL often achieves global convergence without the need of retraining or retuning, while producing consistent and reproducible results due to its deterministic policy. Although FC-RL’s performance depends on sufficient training diversity and is limited to novel scenarios within the known environments, these findings indicate that the proposed method is particularly well-suited for repetitive engineering optimization tasks involving variable problem configurations across multiple systems.