TabQL is a reinforcement learning framework that substitutes a tabular foundation model with in-context capabilities for the parametric Q-network in DQN, with a warm-up phase and theoretical analysis claiming improved sample efficiency.
Distributed real-time scheduling in cloud manufacturing by deep reinforcement learning.IEEE Transactions on Industrial Informatics, 18(12):8999–9007, 2022
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TempoNet uses a slack-quantized Transformer with deep Q-learning and sparse attention to improve deadline fulfillment rates over traditional and neural schedulers in mixed-criticality real-time workloads.
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TabQL: In-Context Q-Learning with Tabular Foundation Models
TabQL is a reinforcement learning framework that substitutes a tabular foundation model with in-context capabilities for the parametric Q-network in DQN, with a warm-up phase and theoretical analysis claiming improved sample efficiency.
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TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs
TempoNet uses a slack-quantized Transformer with deep Q-learning and sparse attention to improve deadline fulfillment rates over traditional and neural schedulers in mixed-criticality real-time workloads.