# Efficient Transformer-based Hyper-parameter Optimization for Resource-constrained IoT Environments

arXiv: https://arxiv.org/abs/2403.12237 · HTML: https://mlsanity.com/paper/2403.12237/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 18 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environment](https://mlsanity.com/paper/2307.12149/) (2023)

## Built on by (most cited first, 2 of 2)

- [Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging Infrastructure](https://mlsanity.com/paper/2503.14799/) (2025)
- [GRPOformer: Advancing Hyperparameter Optimization via Group Relative Policy Optimization](https://mlsanity.com/paper/2509.17105/) (2025)

## Abstract (arXiv)

The hyper-parameter optimization (HPO) process is imperative for finding the best-performing Convolutional Neural Networks (CNNs). The automation process of HPO is characterized by its sizable computational footprint and its lack of transparency; both important factors in a resource-constrained Internet of Things (IoT) environment. In this paper, we address these problems by proposing a novel approach that combines transformer architecture and actor-critic Reinforcement Learning (RL) model, TRL-HPO, equipped with multi-headed attention that enables parallelization and progressive generation of layers. These assumptions are founded empirically by evaluating TRL-HPO on the MNIST dataset and comparing it with state-of-the-art approaches that build CNN models from scratch. The results show that TRL-HPO outperforms the classification results of these approaches by 6.8% within the same time frame, demonstrating the efficiency of TRL-HPO for the HPO process. The analysis of the results identifies the main culprit for performance degradation attributed to stacking fully connected layers. This paper identifies new avenues for improving RL-based HPO processes in resource-constrained environments.