Differentiable MPC for End-to-end Planning and Control

NeurIPS 2018 Brandon AmosIvan Dario Jimenez RodriguezJacob SacksByron BootsJ. Zico Kolter

We present foundations for using Model Predictive Control (MPC) as a differentiable policy class for reinforcement learning in continuous state and action spaces. This provides one way of leveraging and combining the advantages of model-free and model-based approaches... (read more)

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