# A Load-Balancing-Aware Learning Framework for Collaborative UAV-MEC Computation Offloading

## TL;DR

This paper introduces a new framework for managing computation offloading in UAV computing clusters to balance latency and energy use.

## Contribution

The novel MORL-LAPB framework combines reinforcement learning and evolutionary mechanisms for multi-objective UAV-MEC optimization.

## Key findings

- MORL-LAPB reduces offloading latency compared to existing baselines.
- The framework extends task execution duration and improves energy efficiency in UAV clusters.
- It dynamically regulates energy and resource allocation under multi-objective constraints.

## Abstract

Unmanned Aerial Vehicle (UAV) computing clusters face severe operational constraints due to limited computing capabilities and battery capacities, which complicate the simultaneous optimization of low offloading latency, long task endurance, and high cluster efficiency. To address these challenges, this paper proposes a Multi-Objective Reinforcement Learning framework based on Latency and Power Balance (MORL-LAPB). Instead of broad situational awareness descriptions, our framework directly combines a reward-shaping reinforcement learning algorithm with an evolutionary mechanism to construct a closed-loop optimization paradigm. Crucially, in this context, ’balancing’ extends beyond traditional computational workload distribution; it represents a joint optimization that balances task allocation to ensure short service delays while simultaneously equating the energy depletion rates across UAV nodes to maximize overall cluster efficiency and operational duration. By efficiently identifying Pareto optimal trade-offs, MORL-LAPB dynamically regulates UAV energy allocation and computational resource scheduling. Experimental results demonstrate that, compared to RSO, NSO, and DRLSO baselines, the proposed MORL-LAPB significantly reduces offloading latency, extends effective task execution duration, and improves cluster energy efficiency. The framework offers flexible adaptability and long-term sustainability for diverse operational scenarios under strict multi-objective constraints.