aiqbench analysis
This paper addresses the critical challenge of safe exploration in reinforcement learning by proposing SBSRL, an algorithm that enforces safety constraints across finite dynamics samples to guarantee secure operation in continuous domains. By leveraging epistemic uncertainty for exploration rather than explicit bonuses, the method achieves practical safety guarantees and scales effectively to high-dimensional robotic hardware tasks. This work advances the theoretical and empirical foundations necessary for deploying RL agents in real-world environments where failure is not an option.
Sampling-Based Safe Reinforcement Learning
arXiv:2605.19469v2 Announce Type: replace Abstract: Safe exploration remains a fundamental challenge in reinforcement learning (RL), limiting the deployment of RL agents in the real world. We propose Sampling-Based Safe Reinforcement Learning (SBSRL), a model-based RL algorithm that maintains safety throughout the learning process by enforcing constraints jointly across a finite set of dynamics samples. This formulation approximates an intractable worst-case optimization over uncertain dynamics and enables practical safety guarantees in continuous domains. We further introduce an exploration strategy based on constraining epistemic uncertainty, eliminating the need for explicit exploration bonuses. Under regularity conditions, we derive high-probability guarantees of safety throughout learning and a finite-time sample complexity bound for recovering a near-optimal policy. Empirically, SBSRL achieves safe and efficient exploration both in simulation and in real robotic hardware, and readily extends to practical deep-ensemble implementations that scale to high-dimensional continuous control problems.
Abstract:Safe exploration remains a fundamental challenge in reinforcement learning (RL), limiting the deployment of RL agents in the real world. We propose Sampling-Based Safe Reinforcement Learning (SBSRL), a model-based RL algorithm that maintains safety throughout the learning process by enforcing constraints jointly across a finite set of dynamics samples. This formulation approximates an intractable worst-case optimization over uncertain dynamics and enables practical safety guarantees in continuous domains. We further introduce an exploration strategy based on constraining epistemic uncertainty, eliminating the need for explicit exploration bonuses. Under regularity conditions, we derive high-probability guarantees of safety throughout learning and a finite-time sample complexity bound for recovering a near-optimal policy. Empirically, SBSRL achieves safe and efficient exploration both in simulation and in real robotic hardware, and readily extends to practical deep-ensemble implementations that scale to high-dimensional continuous control problems.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
arXiv:2605.19469 [cs.LG]
(or arXiv:2605.19469v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.19469 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 233 words
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