FlowRL: Flow-Augmented Few-Shot Reinforcement Learning for Semi-Structured Sensor Data
This addresses data scarcity in resource-constrained RL applications like DVFS, robotics, and smart grids, offering a scalable but incremental improvement.
The paper tackles the challenge of few-shot reinforcement learning with limited semi-structured sensor data by proposing FlowRL, which uses continuous normalizing flows to generate synthetic data, resulting in up to 35% higher frame rates and faster convergence in a DVFS case study.
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.