Reinforcement Learning Ship Autopilot: Sample efficient and Model Predictive Control-based Approach
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2019
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| _version_ | 1866915071468240896 |
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| author | Cui, Yunduan Osaki, Shigeki Matsubara, Takamitsu |
| author_facet | Cui, Yunduan Osaki, Shigeki Matsubara, Takamitsu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1901_07905 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning Ship Autopilot: Sample efficient and Model Predictive Control-based Approach Cui, Yunduan Osaki, Shigeki Matsubara, Takamitsu Systems and Control 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. |
| title | Reinforcement Learning Ship Autopilot: Sample efficient and Model Predictive Control-based Approach |
| topic | Systems and Control |
| url | https://arxiv.org/abs/1901.07905 |