Sample-and-computation-efficient Probabilistic Model Predictive Control with Random Features
Cheng-Yu Kuo,Yunduan Cui,Takamitsu Matsubara,Cheng-Yu Kuo,Yunduan Cui,Takamitsu Matsubara
Gaussian processes (GPs) based Reinforcement Learning (RL) methods with Model Predictive Control (MPC) have demonstrated their excellent sample efficiency. However, since the computational cost of GPs largely depends on the training sample size, learning an accurate dynamics using GPs result in low control frequency in MPC. To alleviate this trade-off and achieve a sample-and-computation-efficient...
