A framework for simultaneous model predictive control and online parameter estimation is introduced by treating differentiable physics simulators as computational objects for gradient-based joint optimization.
Model-free and bayesian ensembling model-based deep reinforcement learning for particle accelerator con- trol demonstrated on the fermi fel
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MPC and System Identification with Differentiable Physics: Fluid System and Particle Beam Control
A framework for simultaneous model predictive control and online parameter estimation is introduced by treating differentiable physics simulators as computational objects for gradient-based joint optimization.