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COMPARISON BETWEEN DYNAMIC PROGRAMMING AND REINFORCEMENT LEARNING: A CASE STUDY ON MAIZE IRRIGATION MANAGEMENT

Irrigation scheduling is an important decision problem in agriculture. The purpose of the study presented in this paper is to compare dynamic programming (DP) and reinforcement learning (RL) methods for identifying optimal starting irrigation strategies, when a limited amount of water is available for irrigation. Both of the optimization methods use the MODERATO simulator, which includes a growth simulator and an irrigation strategy simulator for maize crops, coupled with a stochastic random weather generator. The results we present illustrate for each of these methods the relations between the number of simulation runs, the size of the discretization and the quality of the approximated optimal strategy that is obtained.