dc.contributor.author | Martinsen, Andreas Bell | |
dc.contributor.author | Lekkas, Anastasios M. | |
dc.date.accessioned | 2019-04-25T11:33:16Z | |
dc.date.available | 2019-04-25T11:33:16Z | |
dc.date.created | 2019-01-14T08:42:17Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | http://hdl.handle.net/11250/2595492 | |
dc.description.abstract | We propose a new framework, based on reinforcement learning, for solving the straight-path following problem for underactuated marine vessels under the influence of unknown ocean current. A dynamic model from the Marine Systems Simulator is employed to simulate the motion of a mariner-class vessel, however the policy search algorithm has no prior knowledge of the system it is assigned to control. A deep neural network is used as function approximator and the deep deterministic policy gradients method is employed to extract a suitable policy that minimizes the cross-track error. Two intuitive reward functions, which in addition prevent noisy rudder behavior, are proposed and compared. The simulation results demonstrate excellent performance, also in comparison with the line-of-sight guidance law. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | International Federation of Automatic Control (IFAC) | nb_NO |
dc.relation.ispartof | 11th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles CAMS 2018 Opatija, Croatia, 10–12 September 2018 | |
dc.title | Straight-Path Following for Underactuated Marine Vessels using Deep Reinforcement Learning | nb_NO |
dc.type | Chapter | nb_NO |
dc.description.version | publishedVersion | nb_NO |
dc.identifier.doi | 10.1016/j.ifacol.2018.09.502 | |
dc.identifier.cristin | 1655782 | |
dc.description.localcode | © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. | nb_NO |
cristin.unitcode | 194,63,25,0 | |
cristin.unitname | Institutt for teknisk kybernetikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |