  In this research we focus on developing a reinforcement learning system for a
challenging task: autonomous control of a real-sized boat, with difficulties
arising from large uncertainties in the challenging ocean environment and the
extremely high cost of exploring and sampling with a real boat. To this end, we
explore a novel Gaussian processes (GP) based reinforcement learning approach
that combines sample-efficient model-based reinforcement learning and model
predictive control (MPC). Our approach, sample-efficient probabilistic model
predictive control (SPMPC), iteratively learns a Gaussian process dynamics
model and uses it to efficiently update control signals within the MPC closed
control loop. A system using SPMPC is built to efficiently learn an autopilot
task. After investigating its performance in a simulation modeled upon real
boat driving data, the proposed system successfully learns to drive a
real-sized boat equipped with a single engine and sensors measuring GPS, speed,
direction, and wind in an autopilot task without human demonstration.
