Financial trading decision model based on deep reinforcement learning for smart agricultural management.

ABSTRACT: This study proposes a decision-making model based on deep reinforcement learning (DRL) for agricultural financial transactions, addressing core challenges such as significant data noise, strong time-series dependence, and limited strategy adaptability. We developed a multifactor dynamic denoising framework by integrating the Grubbs test for outlier detection and the median absolute deviation (MAD) method for noise handling. This framework categorizes agricultural financial indicators into six feature types, significantly enhancing robustness against data noise and improving model reliability. Furthermore, an long short-term memory (LSTM)-enhanced DRL architecture is employed, incorporating a sliding window mechanism to capture market timing features. This framework constructs a transaction

SUBMITTER: Fan D 

PROVIDER: S-EPMC12453850 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature