Reinforcement Learning Boat Autopilot: A Sampleefficient and Model Predictive Control based Approach In this research we focus on developing a reinforcement learning system for a challenging task: autonomous control of a realsized 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 sampleefficient modelbased reinforcement learning and model predictive control (MPC). Our approach, sampleefficient 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 realsized boat equipped with a single engine and sensors measuring GPS, speed, direction, and wind in an autopilot task without human demonstration.

Learningbased Nonlinear Model Predictive Control of Reconfigurable Autonomous Robotic Boats: Roboats This paper presents a Learningbased Nonlinear Model Predictive Control (LBNMPC) algorithm for reconfigurable autonomous vessels to facilitate highaccurate path tracking. Each vessel is designed to latch to a predefined point of another vessel that allows the vessels to form a rigid body. The number of possible configurations of such vessels exponentially grows as the total number of vessels increases, which imposes a technical challenge in modeling and identification. In this work, we propose a framework consisting of a realtime parameter estimator and a feedback control strategy, which is capable of ensuring highaccurate path tracking for any feasible configuration of vessels. Novelty of our method is in that the parameter is estimated online and adjusts control parameters (e.g., cost function and dynamic model) simultaneously to improve pathtracking performance. Through experiments on different configurations of connectedvessels, we demonstrate stability of our proposed approach and its effectiveness in highaccuracy in path tracking.