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In this work, we present a new approach for latent system dynamics and remaining useful life (RUL) estimation of complex degrading systems using generative modeling and reinforcement learning. The main contributions of the proposed method are two-fold. First, we show how a deep generative model can approximate the functionality of high-fidelity simulators and, thus, is able to substitute expensive and complex physics-based models with data-driven surrogate ones. In other words, we can use the generative model in lieu of the actual system as a surrogate model of the system. Furthermore, we show how to use such surrogate models for predictive analytics. Our method follows two main steps. First, we use a deep variational autoencoder (VAE) to learn the distribution over the latent state-space that characterizes the dynamics of the system under monitoring. After model training, the probabilistic VAE decoder becomes the surrogate system model. Then, we develop a scalable reinforcement learning framework using the decoder as the environment, to train an agent for identifying adequate approximate values of the latent dynamics, as well as the RUL.
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This content will become publicly available on May 1, 2026
Mitigating over-exploration in latent space optimization using LES
We develop Latent Exploration Score (LES) to mitigate over-exploration in Latent Space Op- timization (LSO), a popular method for solv- ing black-box discrete optimization problems. LSO utilizes continuous optimization within the latent space of a Variational Autoencoder (VAE) and is known to be susceptible to over- exploration, which manifests in unrealistic solu- tions that reduce its practicality. LES leverages the trained decoder’s approximation of the data distribution, and can be employed with any VAE decoder–including pretrained ones–without addi- tional training, architectural changes or access to the training data. Our evaluation across five LSO benchmark tasks and twenty-two VAE mod- els demonstrates that LES always enhances the quality of the solutions while maintaining high objective values, leading to improvements over ex- isting solutions in most cases. We believe that new avenues to LSO will be opened by LES’ ability to identify out of distribution areas, differentiability, and computational tractability.
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- PAR ID:
- 10635745
- Publisher / Repository:
- ICML
- Date Published:
- ISSN:
- 2640-3498
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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