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DSAC-ICM:用于3D不平坦地形中的路径规划的分布式强化学习框架
Yixin Zhou1, Fan Liu2, Zhixiao Liu3
1School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究介绍了DSAC-ICM,这是一种新的深度强化学习方法,用于在复杂的3D地形中自主移动机器人导航. 它通过减轻价值高估和改善探索以实现更快,更高效的机器人操作来增强路径规划.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 路径规划 路径规划
背景情况:
- 自主移动机器人对于公共安全和国防至关重要.
- 全球3D路径规划不平坦地形对传统方法提出了重大挑战.
- 深度强化学习 (DRL) 面临着价值高估和低效探索的问题.
研究的目的:
- 开发一个先进的DRL框架,用于在具有挑战性的3D环境中对地面机器人进行可靠的导航.
- 为了解决现有的DRL方法的局限性,特别是价值高估和稀疏奖励.
主要方法:
- 拟议的DSAC-ICM:一个分布式软行动者-关键框架与内在好奇心模块 (ICM) 结合在一起.
- 学习了状态动作回报的全部概率分布,以减轻价值高估.
- 整合ICM,为增强对新状态的探索产生内在的回报.
主要成果:
- DSAC-ICM在现实的3D不平坦地形环境中实现了有效的导航能力.
- 与传统算法相比,在路径质量和计算成本之间取得了更好的平衡.
- 与其他DRL基线相比,显示了明显更快的收和更高的回报.
结论:
- 在复杂的3D地形中,DSAC-ICM为自主机器人路径规划提供了强大而高效的解决方案.
- 拟议的方法克服了关键的DRL挑战,为更有能力的地面机器人铺平了道路.
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