GMM-TDQN: Two-Stage Multi-Objective Reinforcement Learning for Large-Scale Edge Server Deployment

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.

STQ-Scheduler: A Secure and Throughput-Aware Deep Reinforcement Learning Framework for QoE-Driven Resource Scheduling in Distributed Video Streaming Systems

. With the rapid growth of large-scale video streaming services, cloud gaming, and edge-assisted media delivery, ensuring high Quality of Experience (QoE) under dynamic network conditions and heterogeneous edge infrastructures has become a critical challenge. In practical systems, issues such as burst traffic, cross-edge latency variability, and noisy or unsafe data streams often lead to suboptimal resource utilization and degraded user experience. Meanwhile, conventional rule-based schedulers and static data processing pipelines are unable to jointly address the challenges of efficient model training and adaptive resource allocation. To tackle these problems, this paper proposes STQ-Scheduler, a secure and throughput-aware deep reinforcement learning framework that integrates high-throughput data processing, Transformer-based QoE prediction, and Proximal Policy Optimization (PPO)-based resource scheduling. The framework incorporates real-time data cleaning, anomaly filtering, and online feature transformation to ensure data quality and prevent data processing from becoming a bottleneck in distributed training. Furthermore, a multi-objective reward function is designed to jointly optimize QoE, latency, throughput, and system cost, enabling adaptive scheduling decisions across distributed edge nodes. Experimental results demonstrate that STQ-Scheduler significantly outperforms baseline methods in distributed video streaming environments. Specifically, it reduces average latency from 215 ms to 162 ms and improves throughput from 910 req/s to 1075 req/s under normal workloads. Under burst conditions, it maintains over 1020 req/s while reducing SLA violation rates from 10.5% to 3.9%, confirming its robustness and effectiveness in QoE-driven resource scheduling.

Advanced deep reinforcement learning techniques for dynamic resource allocation in 5G heterogeneous networks

Deep reinforcement learning for cost-efficient resource management in hybrid cloud environments

Sensitivity and ablation studies confirm stable learning and controllable latency-cost trade-offs, demonstrating that lightweight RL can effectively deliver cost-efficient, adaptive autoscaling in hybrid cloud environments.

Multi-Objective Optimization for Data Center HVAC Systems Based on Edge–Cloud Collaborative Deep Reinforcement Learning

The sustained growth of cloud computing and AI training workloads drives data center expansion. Optimizing their control is therefore critical for reducing operational costs. Edge real-time control is indispensable for guaranteeing thermal safety, data sovereignty, and offline availability. Yet deploying Deep Reinforcement Learning (DRL) in production Heating, Ventilation, and Air Conditioning (HVAC) environments confronts cold-start risks, edge–cloud computational asymmetry, and multi-objective conflicts spanning energy efficiency, electricity cost, and thermal safety. To address these challenges, this paper proposes an edge-cloud collaborative physics-informed reinforcement learning framework for production data center HVAC control. The framework integrates a physics-informed cold-start solution using Adaptive Particle Swarm Optimization (APSO) to generate physically constrained initial policies on a gray-box digital twin without expert demonstration data, a three-time-scale edge–cloud architecture coordinating minute-level edge Soft Actor-Critic (SAC) real-time inference, weekly edge APSO online model identification, daily cloud Non-dominated Sorting Genetic Algorithm III (NSGA-III) thermal storage scheduling, and a constraint-aware safe projection layer that embeds thermal safety hard constraints directly into the neural network policy. The framework is validated through a seven-month production deployment spanning the complete summer-to-winter transition, comprising approximately 3.2 million sensor records and evaluated with rigorous statistical methods.

Intelligent Task Scheduling in Edge-Cloud Environments Using Double Deep Q-Network Reinforcement Learning

Experimental evaluation on a heterogeneous synthetic benchmark demonstrates that the proposed DDQN scheduler reduces SLA violations by approximately 85% relative to Round Robin and 72% relative to the greedy baseline, while achieving superior energy efficiency.