AI Research
Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving
Balancing safe exploration and expert guidance for autonomous vehicle training.
Exploration in reinforcement learning for autonomous driving is inherently unsafe: agents must experience novel behaviors to learn, yet exploration can lead to collisions or off-road driving. We propose an uncertainty-aware framework that leverages expert advice to guide explorat...
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ArXiv CS.AI
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ArXiv CS.AI
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This research mitigates the inherent dangers of reinforcement learning by intelligently gating expert intervention based on real-time uncertainty metrics. By balancing autonomous exploration with expert guidance, the framework creates a safer training path for critical autonomous driving tasks like intersection navigation.
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