Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties
This work addresses control challenges for autonomous surface vehicles, offering a hybrid solution that is incremental in nature.
The paper tackles the problem of controlling autonomous surface vehicles with modeling uncertainties by combining conventional control with deep reinforcement learning, resulting in a method that provides closed-loop stability and improved sample efficiency compared to traditional deep reinforcement learning approaches.
This paper presents a novel model-reference reinforcement learning control method for uncertain autonomous surface vehicles. The proposed control combines a conventional control method with deep reinforcement learning. With the conventional control, we can ensure the learning-based control law provides closed-loop stability for the overall system, and potentially increase the sample efficiency of the deep reinforcement learning. With the 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.