PPO-STGNN: A Proximal Policy Optimization Approach with Spatio-Temporal Graph Neural Networks for DAG Task Scheduling in Cloud-Edge-End Computing

arXiv:2609.03503 · cs.AI · Submitted 2026-09-03 · Read on arXiv

cs.AI

Submitted: 2026-09-03

Updated: 2026-09-03

Terminology

• Reinforcement Learning — 8× in this paper · explained in 4 episodes
• Deep Reinforcement Learning — 6× in this paper · explained in 2 episodes
• Action Space — 3× in this paper · explained in 1 episode
• Reward Function — 2× in this paper · explained in 2 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning (DRL) — 1× in this paper · explained in 2 episodes
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Mobile Edge Computing — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Task Decomposition — 1× in this paper · explained in 2 episodes

Sources

• Goal-Oriented Learning at the Edge: Graph Neural Networks Over-the-Air for Blockage Prediction

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