Open access
Jun 2026
ENERGY-EFFICIENT DEEP REINFORCEMENT LEARNING FOR INTELLIGENT TASK SCHEDULING IN EDGE COMPUTING ENVIRONMENTS
This paper proposes an Energy-Efficient Deep Reinforcement Learning (EE-DRL) framework that optimizes task scheduling while minimizing energy consumption and execution delay, and employs a Deep Q-Network to dynamically allocate computational tasks among heterogeneous edge nodes.
Sophia M. Carter, Rohan V. Iyer, Emilio J. Navarro
· Journal of Computer Science · 0 citations