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