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• The proposed physics-informed Reinforcement Learning framework with Gibbs priors matches oracle-level performance while being approximately 6× faster on the first benchmark environment.result 95%
• The method reaches 94.6% of oracle reward with roughly 200× lower decision time on the second benchmark environment.result 95%
• On the most challenging benchmark, the method improves over a PPO baseline by up to 255% in reward and 284% in survived steps.result 95%
• The method remains about 2.5× faster than a strong specialized engineering baseline on the most challenging benchmark.result 95%
• The framework combines semi-Markov control with a Gibbs prior that encodes the system's physics over the action space.method 90%
• A graph neural network surrogate predicts the post-action overload risk of feasible topology actions.technical 90%
• The physics-informed Gibbs prior both selects a small state-dependent candidate set and reweights policy logits before action selection.method 90%
• The method reduces exploration difficulty and online simulation cost while preserving the flexibility of a learned policy.technical 85%

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