Artificial intelligence and reinforcement learning have become prominent as the field of unmanned driving has grown in popularity.Artificial intelligence equipment has a high level of integration,trainability,and programmability,and it is used extensively in the field of unmanned driving path planning.This paper first reviews previous research on the classical path plan-ning algorithm,then investigates the low efficiency of the Q-Learning method and presents an improved Q-Learning algorithm.The approach models the agent's movement and environment first,then designs the Q-Learning algorithm's reward mechanism,and last-ly specifies the agent's action.The simulation results show that the improved Q-Learning algorithm can effectively improve the move-ment path and work efficiency of the agent.
维普
万方数据