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AI Research
arXiv CS.AI·May 27·8 min read
When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control
TL;DR
Deep reinforcement learning (RL)—machine learning where agents learn by trial and error—is often assumed superior to simpler rule-based methods for resource control tasks like scheduling. This benchmark study directly compared deep RL approaches against carefully tuned baseline algorithms across multiple adaptive resource control problems.
✦ Why It Matters
Engineers can now make data-driven decisions about whether to invest in deep RL or optimize simpler baselines for resource control systems.
Key Takeaways
- Calibrated controllers outperform DRL in cost across all tested workloads.
- DRL algorithms excel in specific scenarios like bursty traffic.
- Discrete-action algorithms reduce constraint violations significantly.
- Focus on baseline calibration and reward design for better RL outcomes.
How It Works
The study employs RLScale-Bench, a reproducible benchmark for evaluating DRL in adaptive resource control. It systematically compares the performance of various DRL algorithms against a calibrated rule-based autoscaler, ensuring matched architectures and training conditions.