Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving
arXiv:2605.30576v1 Announce Type: new Abstract: 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 exploration while avoiding long-term dependence. Advice is triggered when epistemic or aleatoric uncertainty exceeds adaptive thresholds derived from rolling buffers, ensuring advice evolves with the agent's confidence. A commitment-cooldown strategy with a stochastic early-stop
This research addresses a critical limitation in applying reinforcement learning to safety-critical domains like autonomous driving, which is becoming more urgent as RL models grow in complexity and scope.
Improving the safety and efficiency of RL exploration in autonomous systems is crucial for their broader adoption and for enabling more complex, real-world AI applications beyond highly controlled environments.
The proposed framework allows AI agents to learn more safely and effectively by integrating expert advice strategically, potentially accelerating the development and deployment of robust autonomous systems.
- · Autonomous vehicle developers
- · AI safety researchers
- · Logistics and transportation companies
- · Robotics corporations
- · Companies relying on traditional, less adaptable control systems
- · Developers unable to integrate uncertainty-aware methods
Safer and more efficient development cycles for autonomous driving and other safety-critical AI applications.
Increased public and regulatory confidence in AI systems, leading to accelerated deployment in sectors beyond driving.
The development of 'ethical AI' frameworks that prioritize safety and human oversight through advanced uncertainty quantification and expert integration.
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Read at arXiv cs.AI