A Distribution-Aware Flow-Matching for Generating Unstructured Data for Few-Shot Reinforcement Learning

• URL: http://arxiv.org/abs/2409.14178v1
• Date: Sat, 21 Sep 2024 15:50:59 GMT
• Title: A Distribution-Aware Flow-Matching for Generating Unstructured Data for Few-Shot Reinforcement Learning
• Authors: Mohammad Pivezhandi, Abusayeed Saifullah,
• Abstract summary: We introduce a distribution-aware flow matching, designed to generate synthetic unstructured data tailored for few-shot reinforcement learning (RL) on embedded processors. We apply feature weighting through Random Forests to prioritize critical data aspects, thereby improving the precision of the generated synthetic data. Our method provides a stable convergence based on max Q-value while enhancing frame rate by 30% in the very beginning first timestamps.
• Score: 1.0709300917082865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating realistic and diverse unstructured data is a significant challenge in reinforcement learning (RL), particularly in few-shot learning scenarios where data is scarce. Traditional RL methods often rely on extensive datasets or simulations, which are costly and time-consuming. In this paper, we introduce a distribution-aware flow matching, designed to generate synthetic unstructured data tailored specifically for an application of few-shot RL called Dynamic Voltage and Frequency Scaling (DVFS) on embedded processors. This method leverages the sample efficiency of flow matching and incorporates statistical learning techniques such as bootstrapping to improve its generalization and robustness of the latent space. Additionally, we apply feature weighting through Random Forests to prioritize critical data aspects, thereby improving the precision of the generated synthetic data. This approach not only mitigates the challenges of overfitting and data correlation in unstructured data in traditional Model-Based RL but also aligns with the Law of Large Numbers, ensuring convergence to true empirical values and optimal policy as the number of samples increases. Through extensive experimentation on an application of DVFS for low energy processing, we demonstrate that our method provides an stable convergence based on max Q-value while enhancing frame rate by 30\% in the very beginning first timestamps, making this RL model efficient in resource-constrained environments.

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