A Novel Q-Learning Optimization Approach for Flight Path Prediction in Asian Cities

The domains of logistics and transportation have long been interested in the optimization of flight paths between cities. This research aims to use Skyscanner data to estimate the optimal flight path between 42 Asian destination cities using Reinforcement Learning (RL) techniques, notably Q-learning. RL is a great strategy for addressing the Travelling Salesman Problem (TSP) connected to aircraft route optimization because of the distinctive reward structure it provides. The main objective of the proposed research is to create a model that learns to suggest aircraft routes based on factors such as cost, time, and number of intermediate points that maximize benefits. The proposed research work incorporates a novel Q-learning approach for training an RL agent to predict optimal flight paths. The proposed research work showcases the power-fullness of Q-Learning based RL agents in suggesting optimal flight routes and lays the groundwork for future developments in this research domain. The proposed algorithm provides useful information for tourists and business people looking for accurate and affordable flight path forecasts in the Asian region. The various performance metrics such as Reward Accumulation(RA), Episode Length(EL), and Exploration and Exploitation evaluates the proposed model performance and yields an optimal solution.