A Multiagent Q-Learning-Based Optimal Allocation Approach for Urban Water Resource Management System

Water environment system is a complex system, and an agent-based model presents an effective approach that has been implemented in water resource management research. Urban water resource optimal allocation is a challenging and critical issue in water environment systems, which belongs to the resource optimal allocation problem. In this paper, a novel approach based on multiagent Q-learning is proposed to deal with this problem. In the proposed approach, water users of different regions in the city are abstracted into the agent-based model. To realize the cooperation among these stakeholder agents, a maximum mapping value function-based Q-learning algorithm is proposed in this study, which allows the agents to self-learn. In the proposed algorithm, an adaptive reward value function is used to improve the performance of the multiagent Q-learning algorithm, where the influence of multiple factors on the optimal allocation can be fully considered. The proposed approach can deal with various situations in urban water resource allocation. The experimental results show that the proposed approach is capable of allocating water resource efficiently and the objectives of all the stakeholder agents can be successfully achieved. Note to Practitioners-Water resource optimal allocation is an important decision making activity in water resource management systems. This paper sets up a water resource optimal allocation model based on multiagent modeling technology, where different optimal objectives are abstracted into various properties of agents, and a new multiagent Q-learning approach is proposed to deal with the optimal allocation problem in water resource management system. The proposed approach can be used in practical water resource management systems, with any feasible data from the statistical agencies of government and companies. The experimental results demonstrate the effectiveness and efficiency of the agent-based allocation model and the proposed approach based on the novel multiagent Q-learning algorithm.