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This paper presents RL2C, a Q-learning-based algorithm for optimizing laser cutting parameters (focal length, laser power) for optical films, reducing taper size and wastage. Experiments show it reduces optimization steps by up to 12.5% and processing time by up to 81.8% compared to existing RL methods.
This paper presents methods for differentiable parameter optimization of differential-algebraic equations (DAEs) with state-dependent events, comparing automatic differentiation through simulation with explicit discrete-adjoint methods.