FlowRL: Flow-Augmented Few-Shot Reinforcement Learning for Semi-Structured Sensor Data
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914243609100288 |
|---|---|
| author | Pivezhandi, Mohammad Saifullah, Abusayeed |
| author_facet | Pivezhandi, Mohammad Saifullah, Abusayeed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14178 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FlowRL: Flow-Augmented Few-Shot Reinforcement Learning for Semi-Structured Sensor Data Pivezhandi, Mohammad Saifullah, Abusayeed Machine Learning I.2.6; I.2.8 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. |
| title | FlowRL: Flow-Augmented Few-Shot Reinforcement Learning for Semi-Structured Sensor Data |
| topic | Machine Learning I.2.6; I.2.8 |
| url | https://arxiv.org/abs/2409.14178 |