# Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computing

arXiv: https://arxiv.org/abs/2501.15203 · HTML: https://mlsanity.com/paper/2501.15203/

## 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 3 of its 12 references that are in the map.
- First posted on arXiv 25 Jan 2025.

## 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, 3 of 3)

- [A Survey on UAV-enabled Edge Computing: Resource Management Perspective](https://mlsanity.com/paper/2210.06679/) (2022)
- [Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networks](https://mlsanity.com/paper/2501.06242/) (2025)
- [Elastic Composition of Crowdsourced IoT Energy Services](https://mlsanity.com/paper/2011.06771/) (2020)

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

- [Test-Time Adaptive Composition for Machine Learning as a Service (MLaaS) in IoT Environments](https://mlsanity.com/paper/2606.07685/) (2026)
- [OrchNAS: Orchestrated Neural Architecture Search Service for Personalised Federated Edge Intelligence](https://mlsanity.com/paper/2607.22805/) (2026)

## Abstract (arXiv)

Industrial Internet of Things (IIoT) applications demand efficient task offloading to handle heavy data loads with minimal latency. Mobile Edge Computing (MEC) brings computation closer to devices to reduce latency and server load, optimal performance requires advanced optimization techniques. We propose a novel solution combining Adaptive Particle Swarm Optimization (APSO) with Reinforcement Learning, specifically Soft Actor Critic (SAC), to enhance task offloading decisions in MEC environments. This hybrid approach leverages swarm intelligence and predictive models to adapt to dynamic variables such as human interactions and environmental changes. Our method improves resource management and service quality, achieving optimal task offloading and resource distribution in IIoT edge computing.