CAPSULE: Control-Theoretic Action Perturbations for Safe Uncertainty-Aware Reinforcement Learning

📰 ArXiv cs.AI

Learn to apply CAPSULE for safe uncertainty-aware reinforcement learning with control-theoretic action perturbations

advanced Published 28 Apr 2026

Action Steps

Apply control-theoretic action perturbations to reinforcement learning algorithms
Use CAPSULE to ensure safe uncertainty-aware exploration
Evaluate the performance of CAPSULE in high-dimensional systems with unknown dynamics
Compare CAPSULE with existing safe reinforcement learning methods
Implement CAPSULE in a real-world control system to test its safety guarantees

Who Needs to Know This

Researchers and engineers working on reinforcement learning and control theory can benefit from this approach to ensure safe exploration in high-dimensional systems

Key Insight

💡 CAPSULE provides hard constraint-based safety guarantees for reinforcement learning in high-dimensional systems with unknown dynamics

Key Takeaways

Learn to apply CAPSULE for safe uncertainty-aware reinforcement learning with control-theoretic action perturbations

Full Article

Title: CAPSULE: Control-Theoretic Action Perturbations for Safe Uncertainty-Aware Reinforcement Learning

Abstract:
arXiv:2604.23576v1 Announce Type: cross Abstract: Ensuring safe exploration in high-dimensional systems with unknown dynamics remains a significant challenge. Existing safe reinforcement learning methods often provide safety guarantees only in expectation, which can still lead to safety violations. Control-theoretic approaches, in contrast, offer hard constraint-based safety guarantees but typically assume access to known system dynamics or require accurate estimation of control-affine models. I

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