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News/When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control
arxiv
PublishedJune 12, 2026 at 4:00 AM
—neutral

When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2605.26418v2 Announce Type: replace 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 reprod

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Discussion
Mentioned models
06
  • 01
    PPO
  • 02
    DQN
  • 03
    A2C
  • 04
    SAC
  • 05
    TD3
  • 06
    DDPG
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#reinforcement-learning#benchmark#resource-control#evaluation

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Mentioned models
06
  • 01
    PPO
  • 02
    DQN
  • 03
    A2C
  • 04
    SAC
  • 05
    TD3
  • 06
    DDPG
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#reinforcement-learning#benchmark#resource-control#evaluation
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Originally published on arxiv ↗
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