Adaptive Data Harvesting for Efficient Neural Network Learning with Universal Constraints
This work addresses the problem of inefficient sample selection in training neural networks with universal constraints, offering a learning-based alternative to fixed heuristics.
This paper introduces a reinforcement learning-based method for adaptive data harvesting in neural network training, which dynamically adjusts training samples to improve constraint satisfaction and efficiency. The approach achieves improved empirical constraint satisfaction and efficiency on Lyapunov NNs and PINNs.
Training neural networks to satisfy universal constraints over continuous domains poses unique challenges. Common examples include Lyapunov Neural Networks (Lyapunov NNs) and Physics-Informed Neural Networks (PINNs), where analytical solutions are generally either unavailable or overly restrictive. Sample-based methods are therefore commonly used to enforce these constraints, and the choice of samples has a substantial impact on convergence speed, stability, and solution quality. Most existing methods rely on fixed heuristics or handcrafted rules, and are suboptimal in practice. In this paper, we aim to improve upon them by learning, from data and experience, how to dynamically and iteratively adjust the samples in response to the model's evolving learning performance. Trained by reinforcement learning, the learned policy improves empirical constraint satisfaction on test problems while significantly improving efficiency. We validate the approach on both Lyapunov NNs and PINNs, and demonstrate its broader applicability to domains where adaptive input selection is essential for effective training.