Distributed reinforcement learning strategy for multi-objective optimization of fed-batch fermentation process

Fermentation processes optimization involves multiple and conflicting objectives and the features of these processes contain many complexities.In this paper,a design of a Pareto-based distributed Q-learning(PDQL)optimization strategy was presented to solve Pareto optimal flow rate trajectories for the lysine fed-batch fermentation process.Q-learning algorithm and Pareto sorting method were combined to generate the nondominated solution set and to make it approximate the actual Pareto front.The strategy described the relation of multi-objectives with the help of rewards strategy.For enhancing searching capability,multiple randomly initialized groups of agents were used.The result of PDQL optimization was compared to PSO with the aggregated function method to test its performance.