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The Critic Found the Safe Region Before the Equation Tightened It
A scheduled method let reinforcement learning shape the value function before progressively enforcing the governing reach-avoid PDE.
Summary
A scheduled method let reinforcement learning shape the value function before progressively enforcing the governing reach-avoid PDE.
High-dimensional reach-avoid analysis is difficult for grid solvers, while physics-informed networks may settle in poor residual minima and reinforcement learning may violate the governing equation. The proposed schedule starts with temporal-difference actor-critic learning, then adds PDE and boundary losses gradually. Two case studies reached accuracy comparable to successfully trained PINNs while mitigating their reported failure mode; the paper does not yet establish scaling across broad safety-critical systems.
Why it matters
A scheduled method let reinforcement learning shape the value function before progressively enforcing the governing reach-avoid PDE.
Limits and context
- Two case studies reached accuracy comparable to successfully trained PINNs while mitigating their reported failure mode; the paper does not yet establish scaling across broad safety-critical systems.
Key claims
A scheduled method let reinforcement learning shape the value function before progressively enforcing the governing reach-avoid PDE.
Qualification: Two case studies reached accuracy comparable to successfully trained PINNs while mitigating their reported failure mode; the paper does not yet establish scaling across broad safety-critical systems.
Evidence: source-2026-08-19-013
Sources
- arXiv preprint 2608.17117arXiv · primary research
Corrections
No corrections have been recorded for this story.