Purpose

To solve the path planning problem of the intelligent driving vehicular, this paper designs a hybrid path planning algorithm based on optimized reinforcement learning (RL) and improved particle swarm optimization (PSO).

Design/methodology/approach

First, the authors optimized the hyper-parameters of RL to make it converge quickly and learn more efficiently. Then the authors designed a pre-set operation for PSO to reduce the calculation of invalid particles. Finally, the authors proposed a correction variable that can be obtained from the cumulative reward of RL; this revises the fitness of the individual optimal particle and global optimal position of PSO to achieve an efficient path planning result. The authors also designed a selection parameter system to help to select the optimal path.

Findings

Simulation analysis and experimental test results proved that the proposed algorithm has advantages in terms of practicability and efficiency. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.

Originality/value

The authors designed a pre-set operation to reduce the participation of invalid particles in the calculation in PSO. And then, the authors designed a method to optimize hyper-parameters to improve learning efficiency of RL. And then they used RL trained PSO to plan path. The authors also proposed an optimal path evaluation system. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.

中文翻译：

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一种基于强化学习与粒子群优化混合算法的路径规划新方法

目的

针对智能驾驶汽车的路径规划问题，设计了一种基于优化强化学习（RL）和改进粒子群优化（PSO）的混合路径规划算法。

设计/方法/方法

首先，作者优化了 RL 的超参数，使其快速收敛并更有效地学习。然后作者为 PSO 设计了一个预设的操作来减少无效粒子的计算。最后，作者提出了一个可以从 RL 的累积奖励中获得的修正变量；这修正了个体最优粒子的适应度和 PSO 的全局最优位置，以实现有效的路径规划结果。作者还设计了一个选择参数系统来帮助选择最优路径。

发现

仿真分析和实验测试结果证明，该算法在实用性和效率方面具有优势。本研究也预示了强化学习在路径规划方面的研究前景，也是作者下一步的研究方向。

原创性/价值

作者设计了一种预先设定的操作，以减少无效粒子参与 PSO 计算。然后，作者设计了一种优化超参数的方法，以提高 RL 的学习效率。然后他们使用 RL 训练的 PSO 来规划路径。作者还提出了一个最优路径评估系统。本研究也预示了强化学习在路径规划方面的研究前景，也是作者下一步的研究方向。