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Closed-loop feedback optimization for autonomous vehicles using deep reinforcement learning
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AI
DOI：10.1016/j.eswa.2026.131383.png)
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Abstract
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• A novel closed-loop feedback framework optimizes motion planning trajectories and updates decision-making in real time. • A dual-value priority sampling strategy identifies high-value samples from both high-reward and low-reward experiences. • A hybrid action selection mechanism integrates the MOBIL model to guide exploration. • The framework outperforms state-of-the-art models and demonstrates its strong potential for real-world applications.
Keywords:
closed-loop feedback
deep reinforcement learning
motion planning
trajectory optimization
autonomous vehicles
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7.5
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3.0W
Citations:
10.2W
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