Fingerprint Networked Reinforcement Learning via Multiagent Modeling for Improving Decision Making in an Urban Food–Energy–Water Nexus

Food–energy–water (FEW) nexus analyses are critical to sustainable development. Nexus analyses form a unique multiagent decision-making arena that requires using a system engineering approach to simultaneously improve food and water security as well as energy efficiency. To tackle the complexity in decision making within a FEW nexus with respect to dynamic behaviors and interactive logics, we model the FEW nexus as a multiagent system (MAS) under a mixed competitive and cooperative environment from the perspective of a Markov game. Then, we propose a fingerprint networked reinforcement learning (FNRL) framework for the collective learning of a MAS by following the logic flows of human decision making. FNRL can alleviate the problems caused by stationary issues in a MAS environment by integrating a long short-term-memory-driven neural network model into the context of multiagent reinforcement learning (RL) to extract fingerprint information via historical data. Numerical simulations for an urban FEW nexus analysis in Florida (USA) demonstrate that applying the FNRL framework can drive agents in the MAS toward achieving optimality via RL in a dynamic environment.