A Deep Dual 3Q Learning Model Incorporating Nonlinear Greedy Factors

To address the issue that multi-model fusion methods are prone to falling into local optimal solutions, and the accuracy of prediction results declines due to reliance on static policies and model selection when dealing with highdimensional data, this paper proposes an improved deep dual 3Q learning model. The model combines three deep learning models-LSTM, DRF, and Transformer-and optimizes the prediction process using the Q-learning algorithm in reinforcement learning. During model training, a nonlinear greedy factor is introduced, and dynamic adjustment based on the inverse tangent function achieves a balance between exploration and exploitation, preventing over-exploration or the local optimal solution problem. Furthermore, the model employs three Q -value estimation methods for weighted updating, which enhances learning stability and accuracy. Experimental results demonstrate that the proposed model significantly outperforms LSTM, GRU, DRF, Transformer, and 3Q learning models across several evaluation metrics. Based on validation results from a local water quality time-series dataset, the proposed model achieves excellent prediction accuracy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(M A E=0.0204, R M S E=\mathbf{0. 0 2 5 2}$</tex>, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M A P E=\mathbf{0. 0 0 0 2 4})$</tex>. Compared to traditional reinforcement learning methods, the model proposed in this paper shows strong potential for practical applications.