Decentralized and partially decentralized reinforcement learning for designing a distributed wetland system in watersheds

In this paper, we use identical-payoff games of reinforcement learning agents as a framework to solve complex multi-criteria optimization problem of watershed management. Multiple analytical criteria are used to assess design decisions for creating a distributed network of wetlands in the watershed. Decentralized game algorithms of reinforcement learning agents as well as a genetic algorithm based method are used for the analysis. Simulation studies are presented which compare the efficiency of the reinforcement learning approaches with a multi-objective genetic algorithm-based approach.