arXiv:2606.10228v1 Announce Type: new Abstract: Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains. In this paper, we approach safe exploration through the lens of epistemic uncertainty, where the actor's sensitivity to parameter perturbations serves as a practical proxy for regions of high uncertainty. We propose Sharpness-Aware Policy Optimization (SHAPO), a sharpness-aware policy update rule that evaluates gradients at perturbed parameters, making policy updates pessimistic with respect to the actor's epistemic uncertainty. Analyti
The continuous development in reinforcement learning necessitates robust safety mechanisms for real-world deployment, making safe exploration a critical and active area of research.
This development addresses a key bottleneck in deploying AI agents in high-stakes environments, potentially accelerating their adoption in critical sectors by enhancing safety and reliability.
The proposed SHAPO method directly improves the safety of RL agents during exploration by making policy updates more cautious in uncertain regions, which differs from prior approaches focused solely on performance optimization.
- · AI developers
- · Robotics industry
- · Safety-critical industries
- · AI research community
- · Accident-prone AI systems
- · Inefficient safe exploration methods
Refinement of AI agent training methodologies to prioritize safety.
Faster industrial adoption of advanced AI and robotic systems due to enhanced trustworthiness.
Potentially reduced regulatory friction for AI deployments in sensitive applications, paving the way for broader societal integration.
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