  This paper presents a novel model-reference reinforcement learning algorithm
for the intelligent tracking control of uncertain autonomous surface vehicles
with collision avoidance. The proposed control algorithm combines a
conventional control method with reinforcement learning to enhance control
accuracy and intelligence. In the proposed control design, a nominal system is
considered for the design of a baseline tracking controller using a
conventional control approach. The nominal system also defines the desired
behaviour of uncertain autonomous surface vehicles in an obstacle-free
environment. Thanks to reinforcement learning, the overall tracking controller
is capable of compensating for model uncertainties and achieving collision
avoidance at the same time in environments with obstacles. In comparison to
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 using an example of autonomous
surface vehicles.
