  Sampling-based model predictive control (MPC) has found significant success
in optimal control problems with non-smooth system dynamics and cost function.
Many machine learning-based works proposed to improve MPC by a) learning or
fine-tuning the dynamics/ cost function, or b) learning to optimize for the
update of the MPC controllers. For the latter, imitation learning-based
optimizers are trained to update the MPC controller by mimicking the expert
demonstrations, which, however, are expensive or even unavailable. More
significantly, many sequential decision-making problems are in non-stationary
environments, requiring that an optimizer should be adaptable and generalizable
to update the MPC controller for solving different tasks. To address those
issues, we propose to learn an optimizer based on meta-reinforcement learning
(RL) to update the controllers. This optimizer does not need expert
demonstration and can enable fast adaptation (e.g., few-shots) when it is
deployed in unseen control tasks. Experimental results validate the
effectiveness of the learned optimizer regarding fast adaptation.
