Research map / Reinforcement learning / Policy gradient and actor-critic
Queue-Learning: A Reinforcement Learning Approach for Providing Quality of Service
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase the reward trade-off coefficient λ | Queue-Learning DDPG controller · Three-stage tandem queue; T=30 | QoS violation probability | Helped |
| Increase the reward trade-off coefficient λ | Queue-Learning DDPG controller · Three-stage tandem queue; T=30 | average sum service-rate | Hurt |
| Use a smaller time slot length | Queue-Learning DDPG controller · Three-stage tandem queue; training | convergence speed | Helped |
| Use a smaller time slot length | Queue-Learning DDPG controller · Three-stage tandem queue; training | average sum service-rate | Helped |
| Use a smaller time slot length | Queue-Learning DDPG controller · Three-stage tandem queue; test | short-term QoS violation probability stability | Helped |
| Increase the time slot length | Queue-Learning DDPG controller · Three-stage tandem queue; test | short-term QoS violation probability fluctuations | Hurt |
| Choose λ≈14 rather than other tested λ values | Queue-Learning DDPG controller · Three-stage tandem queue; T=30 | average sum service-rate subject to the QoS constraint | Helped |
Closest papers by meaning
- QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers (NeurIPS 2024 · similarity 0.86)
- Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret (NeurIPS 2026 · similarity 0.85)
- Queueing Matching Bandits with Preference Feedback (NeurIPS 2024 · similarity 0.83)
- Hephaestus: Mixture Generative Modeling with Energy Guidance for Large-scale QoS Degradation (NeurIPS 2025 · similarity 0.82)
- Optimal Single-Policy Sample Complexity and Transient Coverage for Average-Reward Offline RL (NeurIPS 2025 · similarity 0.82)
- Near-Optimal Regret-Queue Length Tradeoff in Online Learning for Two-Sided Markets (NeurIPS 2025 · similarity 0.82)
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