Provably Safe, Yet Scalable Reinforcement Learning
Provably Safe, Yet Scalable Reinforcement Learning
This research presents the PS2-RL framework, a novel two-phase architecture for learning provably safe reinforcement learning policies in a scalable manner.
The paper introduces PS2-RL, a two-phase reinforcement learning framework that provides provable safety guarantees for robotic control tasks with state dimensions up to 10 by using a learned backup policy to generate implicit control-invariant sets without explicit synthesis.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
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state dimensions
≤10
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d into any existing training pipeline. We establish theoretical guarantees for the proposed framework and evaluate it on robotic control tasks with state dimensions up to 10, a regime in which prior provably safe RL methods struggle or become impractical.
Open source S6
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d into any existing training pipeline. We establish theoretical guarantees for the proposed framework and evaluate it on robotic control tasks with state dimensions up to 10, a regime in which prior provably safe RL methods struggle or become impractical.
Open source S6
Source：arXiv Robotics — research abstracts · arxiv.org