用生成模型合成传感器数据，提升少样本强化学习效率
FlowRL: Flow-Augmented Few-Shot Reinforcement Learning for Semi-Structured Sensor Data
- 用连续归一化流生成高质量合成数据，保留数据相关性
- 在DVFS场景下帧率提升35%，Q值收敛更快
- 适合资源受限的物联网、机器人等少数据场景
在动态电压频率调节（DVFS）等应用中，传感器数据呈半结构化且样本有限，导致少样本强化学习（RL）面临挑战。本文提出流增强强化学习（FlowRL），利用连续归一化流生成高质量合成数据。通过潜在空间自举提升多样性，结合特征加权流匹配保留关键数据相关性，显著提升样本效率与策略鲁棒性。在NVIDIA Jetson TX2上的实验表明，该方法相比基线实现最高35%的帧率提升，并加速Q值收敛，验证了其在资源受限环境中的有效性。该方法可推广至机器人、智能电网等半结构化数据领域，为少样本RL提供可扩展解决方案。
原文摘要 · Abstract (English)
Reinforcement learning (RL) in few-shot scenarios with limited sensor data is challenging due to insufficient training samples, particularly in applications like Dynamic Voltage and Frequency Scaling (DVFS) where sensor readings are semi-structured with inherent correlations. We propose Flow-Augmented Reinforcement Learning (FlowRL), a novel method that leverages continuous normalizing flows to generate high-quality synthetic data for few-shot RL. By integrating latent space bootstrapping for diversity and feature-weighted flow matching to preserve critical data correlations, FlowRL enhances sample efficiency and policy robustness. Evaluated on a DVFS case study using the NVIDIA Jetson TX2, our approach achieves up to 35\% higher frame rates and faster Q-value convergence compared to baselines, demonstrating its effectiveness in resource-constrained environments. FlowRL generalizes to other semi-structured domains, such as robotics and smart grids, offering a scalable solution for data-scarce RL settings.
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