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Adaptive Reinforcement Learning for Dynamic Configuration Allocation in Pre-Production Testing
Yu Zhu
https://arxiv.org/abs/2510.05147 https://arxiv.org/pd…

Adaptive Reinforcement Learning for Dynamic Configuration Allocation in Pre-Production Testing
Ensuring reliability in modern software systems requires rigorous pre-production testing across highly heterogeneous and evolving environments. Because exhaustive evaluation is infeasible, practitioners must decide how to allocate limited testing resources across configurations where failure probabilities may drift over time. Existing combinatorial optimization approaches are static, ad hoc, and poorly suited to such non-stationary settings. We introduce a novel reinforcement learning (RL) fram…

Comparative Analysis of Parameterized Action Actor-Critic Reinforcement Learning Algorithms for Web Search Match Plan Generation
Ubayd Bapoo, Clement N Nyirenda
https://arxiv.org/abs/2510.03064

Comparative Analysis of Parameterized Action Actor-Critic Reinforcement Learning Algorithms for Web Search Match Plan Generation
This study evaluates the performance of Soft Actor Critic (SAC), Greedy Actor Critic (GAC), and Truncated Quantile Critics (TQC) in high-dimensional decision-making tasks using fully observable environments. The focus is on parametrized action (PA) spaces, eliminating the need for recurrent networks, with benchmarks Platform-v0 and Goal-v0 testing discrete actions linked to continuous action-parameter spaces. Hyperparameter optimization was performed with Microsoft NNI, ensuring reproducibility…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
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- Adaptive Reinforcement Learning for Dynamic Configuration Allocation in Pre-Production Testing
Yu Zhu

Où on rappelle que le transport aérien contribue jusqu’Š 4,2 % du réchauffement planétaire, que ses émissions sont toujours en croissance et qu’il n’existe pas de solution décarbonée crédible pour remplacer le kérosène Š court terme.
#climat #transport