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dc.contributor.authorChahid, Abderrazak
dc.contributor.authorNdoye, Ibrahima
dc.contributor.authorMajoris, John E.
dc.contributor.authorBerumen, Michael L.
dc.contributor.authorLaleg-Kirati, Taous-Meriem
dc.date.accessioned2021-03-22T07:00:00Z
dc.date.available2021-03-22T07:00:00Z
dc.date.issued2021-03-12
dc.identifier.urihttp://hdl.handle.net/10754/668188
dc.description.abstractThis paper studies the fish growth trajectory tracking via reinforcement learning under a representative bioenergetic growth model. Due to the complex aquaculture condition and uncertain environmental factors such as temperature, dissolved oxygen, un-ionized ammonia, and strong nonlinear couplings, including multi-inputs of the fish growth model, the growth trajectory tracking problem can not be efficiently solved by the model-based control approaches in precision aquaculture. To this purpose, we formulate the growth trajectory tracking problem as sampled-data optimal control using discrete state-action pairs Markov decision process. We propose two Q-learning algorithms that learn the optimal control policy from the sampled data of the fish growth trajectories at every stage of the fish life cycle from juveniles to the desired market weight in the aquaculture environment. The Q-learning scheme learns the optimal feeding control policy to fish growth rate cultured in cages and the optimal feeding rate control policy with an optimal temperature profile for the aquaculture fish growth rate in tanks. The simulation results demonstrate that both Q-learning strategies achieve high trajectory tracking performance with less amount feeding rates.
dc.description.sponsorshipThe authors would like to thank Professor Jeff Shamma from King Abdullah University of Science and Technology (KAUST) for helpful discussions and guidance on reinforcement learning.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2103.07251.pdf
dc.rightsArchived with thanks to arXiv
dc.titleFish Growth Trajectory Tracking via Reinforcement Learning in Precision Aquaculture
dc.typePreprint
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentEstimation, Modeling and ANalysis Group
dc.contributor.departmentMarine Science Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentRed Sea Research Center (RSRC)
dc.contributor.departmentReef Ecology Lab
dc.eprint.versionPre-print
dc.identifier.arxivid2103.07251
kaust.personChahid, Abderrazak
kaust.personNdoye, Ibrahima
kaust.personMajoris, John Edwin
kaust.personBerumen, Michael L.
kaust.personLaleg-Kirati, Taous-Meriem
refterms.dateFOA2021-03-22T07:01:05Z


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