Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles

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This paper presents a novel model-reference reinforcement learning control method for uncertain autonomous surface vehicles. The proposed control combines a conventional model-based control method with deep reinforcement learning. With the conventional model-based control, we can ensure the learning-based control law provides closed-loop stability for the trajectory tracking control of the overall system, and increase the sample efficiency of the deep reinforcement learning. With reinforcement learning, we can directly learn a control law to compensate for modeling uncertainties. In the proposed control, a nominal system is employed for the design of a baseline control law using a conventional control approach. The nominal system also defines the desired performance for uncertain autonomous vehicles to follow. In comparison with traditional deep reinforcement learning methods, our proposed learning-based control can provide stability guarantees and better sample efficiency. We demonstrate the performance of the new algorithm via extensive simulation results.