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This research aims to develop an intelligent route optimization framework for agricultural heritage tourism using deep reinforcement learning.

Intelligent route optimization for agricultural heritage tourism using deep reinforcement learning

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Randomized trial demonstrates improved route efficiency in agricultural heritage tourism, suggesting enhanced sustainability management.

This research aims to develop an intelligent route optimization framework for agricultural heritage tourism using deep reinforcement learning.
Introduced a Dueling Double Deep Q-Network (D3QN) framework for route optimization.
Defined the routing problem as a dynamic Markov Decision Process incorporating tourist preferences and sustainability measures.
All experiments conducted in a simulated environment without real-world deployment.
Achieved 18.5% higher tourist satisfaction (p < 0.001, d = 1.42) compared to baseline methods.
Enhanced route efficiency by 23.3% (p < 0.001, d = 1.67) over traditional genetic algorithms.
Improved compliance with sustainability indicators by 31.6% (p < 0.001, d = 1.89) during real-time route inference.

Jiya Sun (2026) studied this question.

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