Preprint
Jun 2026
Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networks
A two-timescale multi-layer deep reinforcement learning framework with a latent action space (2T-MDRL-LA) to jointly optimize service placement, user association, computational delegation, task offloading, and user transmit power and achieves near-optimal performance compared to branch-and-bound solutions.
V. Son, Van-Dinh Nguyen, Ngoc Hung Nguyen et al.
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