Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market

Abstract

Yearly population growth will lead to a significant increase in agricultural production in the coming years. Twenty-first century agricultural producers will be facing the challenge of achieving food security and efficiency. This must be achieved while ensuring sustainable agricultural systems and overcoming the problems posed by climate change, depletion of water resources, and the potential for increased erosion and loss of productivity due to extreme weather conditions. Those environmental consequences will directly affect the price setting process. In view of the price oscillations and the lack of transparent information for buyers, a multi-agent system (MAS) is presented in this article. It supports the making of decisions in the purchase of sustainable agricultural products. The proposed MAS consists of a system that supports decision-making when choosing a supplier on the basis of certain preference-based parameters aimed at measuring the sustainability of a supplier and a deep Q-learning agent for agricultural future market price forecast. Therefore, different agri-environmental indicators (AEIs) have been considered, as well as the use of edge computing technologies to reduce costs of data transfer to the cloud. The presented MAS combines price setting optimizations and user preferences in regards to accessing, filtering, and integrating information. The agents filter and fuse information relevant to a user according to supplier attributes and a dynamic environment. The results presented in this paper allow a user to choose the supplier that best suits their preferences as well as to gain insight on agricultural future markets price oscillations through a deep Q-learning agent.

Crowdsourcing food security: introducing food choice derivatives for sustainability

“…Farmers risk losing money if the price of their crop falls before harvest and sale. Futures markets are an important source of price information for farmers although only a small percentage of farmers directly trade futures (Pérez-Pons et al, 2021 ). Farmers may minimize risk by selling futures contracts which guarantee the receipt of the predetermined price.…”

“…Integrating environmental, economic, and social attributes has increased in popularity when selecting suppliers and sourcing processes (Azadnia et al, 2015 ; Ghadimi et al, 2018 , 2019 ) which become part of price setting. A multi-agent system (MAS) has been proposed to support supplier selection based on supplier sustainability informed by a deep Q-learning agent for agricultural future market price forecasting (Pérez-Pons et al, 2021 ). Mechanisms for crowdsourcing societal tradeoffs as part of computational social choice have been proposed (Conitzer et al, 2015 ).…”

Internet of things and agent-based system to improve water use efficiency in collective irrigation

“…Some research reports using smartphones and sensors to remotely monitor the soil condition and enable smart irrigation [5]. A more complex system combines IoT and artificial intelligence techniques such as machine learning [6], Fuzzy logic [7], deep Q-learning [8], artificial neural network [9] and Multi-Agent systems [10] [11] , and to handle diverse aspects, e.g., irrigation, fertilization, or pesticide treatment and so on. In what follows we analyze and discuss succinctly several studies that use a multi-agent system and IoT to perform intelligent farming systems.…”

Generative Artificial Intelligence: Fundamentals

“…It was a major concept that brought about a radical change in the way we deal with information. These networks use machine learning techniques in a somewhat different way to how they were originally conceived Raul Garcia S., and Pablo Chamoso (Bengio, 2009;Pérez-Pons et al, 2021;Hernández et al, 2021). Unlike other models, they have multiple hidden layers that allow features and patterns to be extracted from the input data in an increasingly complex and abstract manner (Parikh et al, 2022).…”