Angel Romero, Yunlong Song, Davide Scaramuzza, Actor-Critic Model Predictive Control, IEEE International Conference on Robotics and Automation, Yokohama, 2024 arXiv:2306.09852 [cs.RO].
An open research question in robotics is how
to combine the benefits of model-free reinforcement learning
(RL)—known for its strong task performance and flexibility in
optimizing general reward formulations—with the robustness
and online replanning capabilities of model predictive control
(MPC). This paper provides an answer by introducing a new
framework called Actor-Critic Model Predictive Control. The
key idea is to embed a differentiable MPC within an actor-
critic RL framework. The proposed approach leverages the
short-term predictive optimization capabilities of MPC with
the exploratory and end-to-end training properties of RL. The
resulting policy effectively manages both short-term decisions
through the MPC-based actor and long-term prediction via
the critic network, unifying the benefits of both model-based
control and end-to-end learning. We validate our method in
both simulation and the real world with a quadcopter platform
across various high-level tasks. We show that the proposed
architecture can achieve real-time control performance, learn
complex behaviors via trial and error, and retain the predictive
properties of the MPC to better handle out of distribution
behaviour.