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Emilio J. Navarro

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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 · 0 citations