QECO
QECO is a decentralized QoE-Oriented Computation Offloading algorithm for Mobile Edge Computing (MEC) systems, implemented in Python. It addresses computation task offloading where strict task processing deadlines and energy constraints can degrade user experience. QECO formulates the offloading problem as a Markov Decision Process (MDP) to maximize long-term quality of experience for each user individually. The algorithm uses Deep Reinforcement Learning, integrating a Dueling Double Deep Q-Network (D3QN) model with Long Short-Term Memory (LSTM) networks to capture MEC environment dynamics, including user behavior and edge server workloads. It enables mobile devices to make independent offloading decisions without requiring knowledge of other devices choices, balancing QoE factors based on individual device requirements while considering dynamic edge node workloads. Simulations show QECO increases completed tasks by up to 14.4%, reduces task delay by 9.2%, cuts energy consumption by 6.3%, and achieves an aver