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Composite-Gradient Learning for Shared Control Authority Between Deep Reinforcement Learning and Model Predictive Control

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

Integrated deep reinforcement learning (DRL) and model predictive control (MPC) methods are increasingly used to control autonomous systems by combining their complementary capabilities. DRL learns control policies through interaction with the environment. MPC uses a system model to optimize control inputs while accounting for constraints. In DRL-MPC frameworks with shared control authority, both the DRL agent and the MPC controller each determine part of the control inputs. However, common learning formulations treat MPC as part of the environment and therefore do not explicitly account for M

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