Abstract: Aiming at problems of slow convergence speed and low learning efficiency of traditional algorithm, intelligent algorithm and reinforcement learning algorithm in automated guided vehicle (AGV) path planning, a heuristic reinforcement learning algorithm was proposed. For the traditional Q(λ) algorithm, the heuristic reward function and heuristic action selection strategy were designed to strengthen the agent's exploration of high-quality behaviors and improve the learning efficiency of the algorithm. Through the simulation and contrast experiments, the improved Q(λ) heuristic reinforcement learning algorithm has advantages in exploring times, planning time, path length and path corner.