Provably Safe, Yet Scalable Reinforcement Learning
Overview
This research presents the PS2-RL framework, a novel two-phase architecture for learning provably safe reinforcement learning policies in a scalable manner.
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Reported quantity · state dimensions: 10 other · Basis not reported
“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.”
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- arXiv Robotics — research abstractsSignalProvably 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.
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