Research on the Agricultural Machinery Path Tracking Method Based on Deep Reinforcement Learning

With the rapid development of information technology, industry and service industries have achieved rapid development in recent years. Then, looking at the development of agriculture, the popularity of informatization lags far behind industry and service industries, directly hindering the digital development of agriculture. Starting from the current agricultural machinery driving operation scene, this paper carried out a simplified research on the traditional agricultural machinery driving operation method through the agricultural machinery kinematics model, and based on the related theory of deep reinforcement learning to study the agricultural machinery path tracking in the agricultural operation scene, it carried out the controller design, built the agricultural machinery autonomous path tracking framework operating mechanism under deep reinforcement learning, and further researched through experimental design and found that the agricultural machinery autonomous path tracking control can achieve better automatic control after empirical learning. I-DQN algorithm enables agricultural robots to adapt to the environment faster when performing path tracking, which improves the performance of path tracking. It has important guiding significance for further promoting the automatic navigation and control of agricultural machinery to realize the efficient operation of agricultural mechanization.