  This 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.
