Sampling-Based Safe Reinforcement Learning
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Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: Sampling-Based Safe Reinforcement Learning. This research proposes Sampling-Based Safe Reinforcement Learning (SBSRL), a model-based RL algorithm that maintains safety throughout the learning process by enforcing constraints across a finite set of dynamics samples…
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- arXiv Robotics — research abstractsSampling-Based Safe Reinforcement Learning
This research proposes Sampling-Based Safe Reinforcement Learning (SBSRL), a model-based RL algorithm that maintains safety throughout the learning process by enforcing constraints across a finite set of dynamics samples. The method approximates an intractable worst-case optimization over uncertain dynamics, enabling practical safety guarantees in continuous domains. It also introduces an exploration strategy that eliminates the need for explicit exploration bonuses. Empirically, SBS,RL achieves safe and efficient exploration in both simulation and real robotic hardware, and extends to deep-ensemble implementations for high-dimensional continuous control.
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