What question did this study set out to answer?

The aim is to address uncertainty in spatiotemporal forecasting using Neutrosophic Deep Q-Network (N-DQN).

March 4, 2026Neutrosophic Systems with Applications0 citations

N-DQN: Neutrosophic Deep Q-Network for Uncertainty-Aware Forecasting and Decision Optimization

Key Points

The aim is to address uncertainty in spatiotemporal forecasting using Neutrosophic Deep Q-Network (N-DQN).
Developed a framework combining neutrosophic logic and deep reinforcement learning.
Utilized membership functions for truth, indeterminacy, and falsity in data modeling.
Implemented attention-guided preprocessing and horizon-aware optimization techniques.
Conducted experiments on benchmark traffic datasets (METR-LA and PEMS-BAY).
Demonstrated improved forecasting accuracy compared to existing methods.
Reduced error rates in predictions across tested datasets.
Showed scalability and resilience for real-world applications.

Abstract

Uncertainty remains a critical challenge in dynamic spatiotemporal forecasting. This study proposes the Neutrosophic Deep Q-Network (N-DQN), a framework that integrates neutrosophic logic with deep reinforcement learning to enhance decision optimization under uncertainty. Features are modeled through truth, indeterminacy, and falsity membership functions, enabling robust handling of ambiguous data. The framework incorporates attention-guided preprocessing and horizon-aware optimization to adapt predictions across short- and long-term intervals. Experiments on benchmark traffic datasets (METR-LA and PEMS-BAY) demonstrate improved forecasting accuracy and reduced error rates compared with established baselines. The results highlight the scalability and resilience of N-DQN, positioning it as a promising approach for real-world applications in intelligent transportation, healthcare, and other dynamic AI systems.

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