A learned policy rollout is refined by one Newton step via Riccati recursion, yielding quadratic reduction in suboptimality for nonlinear MPC, shown on quadcopter trajectory tracking.
Learning Lyapunov terminal costs from data for complexity reduction in nonlinear model predictive control
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Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control
A learned policy rollout is refined by one Newton step via Riccati recursion, yielding quadratic reduction in suboptimality for nonlinear MPC, shown on quadcopter trajectory tracking.