To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing

arXiv:2411.07086 · cs.AI, cs.LG, cs.SY, eess.SY · Submitted 2024-11-11 · Read on arXiv

cs.AI, cs.LG, cs.SY, eess.SY

Submitted: 2024-11-11

Updated: 2024-11-11

Journal ref: IEEE International Conference on Communications, Montreal, QC, Canada, 2025, pp. 2352-2357

DOI: 10.1109/ICC52391.2025.11160980

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Mobile Edge Computing — 8× in this paper · explained in 2 episodes
Deep Reinforcement Learning — 7× in this paper · explained in 2 episodes
Reinforcement Learning — 7× in this paper · explained in 4 episodes
Artificial Intelligence — 5× in this paper
Benchmark — 4× in this paper · explained in 2 episodes
Continual Learning — 4× in this paper · explained in 2 episodes
Action Space — 3× in this paper · explained in 1 episode
Adam — 3× in this paper · explained in 2 episodes
Artificial Intelligence (AI) — 3× in this paper · explained in 2 episodes
Deep Reinforcement Learning (DRL) — 3× in this paper · explained in 2 episodes
Adaptation — 2× in this paper · explained in 1 episode
Adaptive Strategy — 2× in this paper · explained in 1 episode
Adaptive Training Strategy — 2× in this paper
Q-learning — 2× in this paper · explained in 3 episodes
Accuracy — 1× in this paper · explained in 1 episode
Core — 1× in this paper · explained in 2 episodes
Markov Decision Process (MDP) — 1× in this paper · explained in 10 episodes
National Recovery and Resilience Plan — 1× in this paper
Periodic Training Strategy — 1× in this paper
Prioritized Experience Replay — 1× in this paper
Shortest Job First — 1× in this paper
Time Difference — 1× in this paper

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