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基于改进的DQN算法对AGV路径规划的研究

Qian Xiao¹, Tengteng Pan¹, Kexin Wang¹

¹School of Intelligent Science Information Engineering, Shenyang University, Shenyang 110044, China.

Sensors (Basel, Switzerland)

|August 14, 2025

概括

本研究介绍了B-PER深度Q网络 (DQN) 算法,用于自动引导车辆 (AGV) 系统中的自适应路径规划. 新方法提高了复杂环境中的融合速度和适应能力,克服了传统深度强化学习的局限性.

科学领域:

机器人和自动化机器人和自动化
人工智能的人工智能
机器学习机器学习

背景情况:

传统的深度强化学习 (DRL) 方法在复杂的环境中表现出缓慢的融合和较差的适应性.
这些方法往往导致在自动驾驶汽车 (AGV) 系统应用中找到低于最佳的解决方案 (局部最佳).

研究的目的:

为AGV系统提出一个改进的自适应路径规划算法,使用一种新的深度Q网络 (DQN) 方法.
提高基于DRL的路径规划的效率,适应性和融合速度.

主要方法:

开发了B-PER深度Q网络 (DQN) 算法,将波尔茨曼策略的动态温度调整机制纳入其中.
整合了优先经验重复 (PER) 机制,以提高培训效率和任务多样性.
设计了一种精致的多目标奖励功能,包括方向指导,步骤惩罚和终点奖励.

主要成果:

与同一个环境中的其他算法相比,B-PER DQN算法显示出更高的成功率.
实现了更快的融合速度,表明学习效率有所提高.
验证了算法作为复杂路径规划任务的高效和适应性解决方案.

结论:

拟议的B-PER DQN算法有效地解决了AGV路径规划中的传统DRL方法的局限性.
适应机制和精细的奖励功能有助于在复杂和动态的环境中提供卓越的性能.
这项研究为自动驾驶系统的智能路径规划提供了有前途的方法.

关键词:

自动引导车辆自动引导车辆深度Q网络是一个深度Q网络.深度强化学习的学习.路径规划路径规划路径规划

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