The Reinforcement Learning (RL) algorithm, renowned for its robust learning capability and search stability, has garnered significant attention and found extensive application in Automated Guided Vehicle (AGV) path planning. However, RL planning algorithms encounter challenges stemming from the substantial variance of neural networks caused by environmental instability and significant fluctuations in system structure. These challenges manifest in slow convergence speed and low learning efficiency. To tackle this issue, this paper presents the Particle Filter-Double Deep Q-Network (PF-DDQN) approach, which incorporates the Particle Filter (PF) into multi-AGV reinforcement learning path planning. The PF-DDQN method leverages the imprecise weight values of the network as state values to formulate the state space equation. Through the iterative fusion process of neural networks and particle filters, the DDQN model is optimized to acquire the optimal true weight values, thus enhancing the algorithm's efficiency. The proposed method's effectiveness and superiority are validated through numerical simulations. Overall, the simulation results demonstrate that the proposed algorithm surpasses the traditional DDQN algorithm in terms of path planning superiority and training time indicators by 92.62% and 76.88%, respectively. In conclusion, the PF-DDQN method addresses the challenges encountered by RL planning algorithms in AGV path planning. By integrating the Particle Filter and optimizing the DDQN model, the proposed method achieves enhanced efficiency and outperforms the traditional DDQN algorithm in terms of path planning superiority and training time indicators.

翻译：强化学习算法以其强大的学习能力和搜索稳定性而闻名，在自动导引车路径规划中引起了广泛关注并得到应用。然而，由于环境不稳定性和系统结构的显著波动导致神经网络方差较大，强化学习规划算法面临收敛速度慢和学习效率低等挑战。为解决这一问题，本文提出了粒子滤波-双深度Q网络方法，将粒子滤波引入多AGV强化学习路径规划中。PF-DDQN方法利用网络的不精确权值作为状态值来构建状态空间方程，通过神经网络与粒子滤波的迭代融合过程，优化DDQN模型以获取最优真实权值，从而提升算法效率。通过数值仿真验证了所提方法的有效性和优越性。总体而言，仿真结果表明，所提算法在路径规划优越性和训练时间指标上分别比传统DDQN算法提升了92.62%和76.88%。综上，PF-DDQN方法解决了强化学习规划算法在AGV路径规划中遇到的挑战。通过整合粒子滤波并优化DDQN模型，所提方法实现了更高的效率，并在路径规划优越性和训练时间指标上优于传统DDQN算法。