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• W2913442308 abstract "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." @default.
• W2913442308 created "2019-02-21" @default.
• W2913442308 creator A5011048472 @default.
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• W2913442308 date "2019-01-23" @default.
• W2913442308 modified "2023-09-27" @default.
• W2913442308 title "Reinforcement Learning Ship Autopilot: Sample efficient and Model Predictive Control-based Approach" @default.
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• W2913442308 hasPublicationYear "2019" @default.
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