Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties

• URL: http://arxiv.org/abs/2003.13839v1
• Date: Mon, 30 Mar 2020 22:02:13 GMT
• Title: Model-Reference Reinforcement Learning Control of Autonomous Surface Vehicles with Uncertainties
• Authors: Qingrui Zhang and Wei Pan and Vasso Reppa
• Abstract summary: The proposed control combines a conventional control method with deep reinforcement learning. With the reinforcement learning, we can directly learn a control law to compensate for modeling uncertainties. In comparison with traditional deep reinforcement learning methods, our proposed learning-based control can provide stability guarantees and better sample efficiency.
• Score: 1.7033108359337459
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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