arXiv:2605.26418v1 Announce Type: new Abstract: A properly calibrated rule-based autoscaler can beat every one of six mainstream deep reinforcement learning (DRL) algorithms on cost across every workload we test - so when, if ever, does DRL actually help? We study this in RLScale-Bench, a reproducible benchmark and evaluation protocol for DRL on adaptive resource control, where an agent allocates compute to a dynamic workload under cost and service-level constraints. We evaluate PPO, DQN, A2C, SAC, TD3, and DDPG under matched architectures, training budgets, and reward functions against a cali
This research provides a timely evaluation of Deep Reinforcement Learning's practical applicability in resource control, contrasting its performance against established baselines, as the industry increasingly explores its deployment.
A strategic reader should care because this study challenges the prevailing assumption that DRL is always superior, highlighting the need for rigorous benchmarking and potentially re-evaluating investment and development priorities in AI-driven resource management.
The conventional wisdom regarding DRL's universal superiority in certain adaptive control tasks is now under direct scrutiny, suggesting that simpler, calibrated methods can be more effective for cost efficiency.
- · Companies with expertise in traditional control theory
- · Cloud resource management platforms focused on efficiency
- · Researchers developing rigorous AI benchmarking frameworks
- · DRL-focused startups without practical validated applications
- · Investors funding DRL solutions without strong comparative benchmarks
- · Developers solely relying on DRL for resource optimization
Industry adoption of DRL for resource control may slow down, with a renewed focus on proven, simpler solutions where applicable.
There will be increased demand for hybrid AI systems that combine the robustness of traditional control with the adaptability of DRL for complex, dynamic environments.
The development of AI for critical infrastructure could prioritize interpretability and reliability over pure algorithmic sophistication, influencing future design paradigms.
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Read at arXiv cs.LG