A double DRL architecture selects forecasting models dynamically from a committee and introduces average-reward-convergence early stopping, demonstrating robustness on grocery sales and snack demand datasets.
An ensemble framework for probabilistic short-term load forecasting based on bitcn and deep attention networks.TechRxiv, 2025(0319), 2025
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Designing a double deep reinforcement learning selection tool for resilient demand prediction
A double DRL architecture selects forecasting models dynamically from a committee and introduces average-reward-convergence early stopping, demonstrating robustness on grocery sales and snack demand datasets.