Optimization Path Planning Algorithm Based on Deep Reinforcement Learning

With the acceleration of urbanization and the growth of traffic demand, traffic congestion has become more and more prominent. In the field of path planning, Deep Reinforcement Learning (DRL) can effectively deal with the complex traffic environment and learn the optimal path selection strategy. This article aims to explore the optimal path planning algorithm based on DRL to improve the accuracy and efficiency of path planning. The algorithm estimates the state function by establishing a deep neural network, and uses reinforcement learning to optimize the strategy, so as to fully consider the actual traffic flow and provide accurate route suggestions for users. The experimental results show that the optimized path planning algorithm based on DRL has smaller path-finding error and higher path-finding accuracy. After many iterations, the routing accuracy of the algorithm can even reach more than 96%. Therefore, the optimized path planning algorithm based on DRL is an effective path planning method, which can deal with complex traffic scene information and optimize the path planning effect.