# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 3 Multi-Objective Path Planning
### Optimization Method  
This experiment utilizes Deep Q-Learning (DQN) in conjunction with the A* algorithm for multi-objective path optimization. By combining the autonomous decision-making ability of the agent with the advantages of heuristic search, a dual-path generation mechanism is established to explore the multi-objective optimization space. The A* algorithm ensures the basic feasibility of the solution, while the DQN network learns dynamic optimization strategies through environmental interactions. Together, they generate a set of paths that satisfy multi-objective constraints. The method integrates the advantages of both path generation approaches through the experience replay mechanism: A* paths serve as high-quality samples to accelerate DQN convergence, while the DQN-explored paths iteratively optimize the Q-value function through the Bellman equation, dynamically adjusting the weights of the path evaluation metrics. For each generated path, a multi-objective value network is employed to simultaneously assess various path attributes (such as distance, risk factor, etc. ), and the advantage function decomposition technique is applied to achieve trade-off optimization across multiple objectives. This hybrid path generation strategy ensures the feasibility of the solution set, while leveraging the generalization ability of deep neural networks to explore a broader solution space. In the first phase, the A* algorithm is employed for global path search. The core of this approach lies in the introduction of a multi-objective dynamic weighted cost function, which comprehensively evaluates five optimization objectives along the path: total distance, congested distance, travel time, risk index, and coverage of avoidance areas. The A*algorithm calculates the priority of each node using the heuristic evaluation function (f (n) = g (n) + h (n)). During path expansion, the algorithm dynamically determines whether an edge belongs to an avoidance area or restricted road segment based on its attributes, and adjusts the weights of each optimization objective according to the current travel scenario. This adjustment reduces the bias in overall path selection caused by the high correlation between objectives. Ultimately, the A* algorithm generates a globally feasible path that satisfies multi-objective constraints (such as minimizing congestion, travel time, or risk), providing a framework for subsequent local optimization. After obtaining the initial path from the A* algorithm, the second phase introduces the deep Q-network (DQN) model from deep reinforcement learning to perform local path optimization. In this phase, the A* path serves as the input, and the state space is constructed by incorporating current traffic conditions, road status, traffic light information, reachable neighboring nodes, and obstacle avoidance requirements, which serve as environmental perception data. The Q-value function is then used to assess the expected return of the current node under different actions (i.e., moving to the next neighboring node), thus enabling dynamic optimization of the selection of local nodes, see Algorithm 1.