  In this paper, we present the use of Reinforcement Learning (RL) based on
Robust Model Predictive Control (RMPC) for the control of an Autonomous Surface
Vehicle (ASV). The RL-MPC strategy is utilized for obstacle avoidance and
target (set-point) tracking. A scenario-tree robust MPC is used to handle
potential failures of the ship thrusters. Besides, the wind and ocean current
are considered as unknown stochastic disturbances in the real system, which are
handled via constraints tightening. The tightening and other cost parameters
are adjusted by RL, using a Q-learning technique. An economic cost is
considered, minimizing the time and energy required to achieve the ship
missions. The method is illustrated in simulation on a nonlinear 3-DOF model of
a scaled version of the Cybership 2.
