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This paper studies the fish growth trajectory tracking via reinforcementlearning under a representative bioenergetic growth model. Due to the complexaquaculture condition and uncertain environmental factors such as temperature,dissolved oxygen, un-ionized ammonia, and strong nonlinear couplings, includingmulti-inputs of the fish growth model, the growth trajectory tracking problemcan not be efficiently solved by the model-based control approaches inprecision aquaculture. To this purpose, we formulate the growth trajectorytracking problem as sampled-data optimal control using discrete state-actionpairs Markov decision process. We propose two Q-learning algorithms that learnthe optimal control policy from the sampled data of the fish growthtrajectories at every stage of the fish life cycle from juveniles to thedesired market weight in the aquaculture environment. The Q-learning schemelearns the optimal feeding control policy to fish growth rate cultured in cagesand the optimal feeding rate control policy with an optimal temperature profilefor the aquaculture fish growth rate in tanks. The simulation resultsdemonstrate that both Q-learning strategies achieve high trajectory trackingperformance with less amount feeding rates.

Fish Growth Trajectory Tracking via Reinforcement Learning in Precision Aquaculture