一个自我学习的蒙特卡洛树搜索算法用于机器人路径规划
1Academy for Engineering and Technology, Fudan University, Shanghai, China.
Frontiers in neurorobotics
|July 24, 2023
概括
本研究介绍了一种自学蒙特卡罗树搜索 (SL-MCTS) 算法,该算法可以增强单人游戏中的问题解决能力. 与传统方法相比,SL-MCTS显著提高了路径质量,并减少了时间消耗.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
背景情况:
- 蒙特卡罗树搜索 (MCTS) 是一种常见的算法,用于复杂问题的决策.
- 传统的MCTS在单一玩家场景中可能是低效的,需要提高融合速度和搜索效率.
- 现有的路径规划算法往往缺乏持续自我改进的能力.
研究的目的:
- 提出一种新的自学MCTS算法 (SL-MCTS),以提高单人场景中的性能.
- 通过使用神经网络来提高MCTS的融合速度和搜索效率.
- 通过自我学习,不断提高解决问题的能力.
主要方法:
- 开发了一个自学蒙特卡罗树搜索 (SL-MCTS) 算法.
- 集成了一个双分支神经网络 (PV-Network) 来预测搜索方向和节点值,取代了MCTS推出过程.
- 实施了一种自我学习机制,将当前模型性能与历史最佳模型进行比较,以指导优化.
主要成果:
- 与传统的MCTS和单人MCTS相比,SL-MCTS在机器人路径规划方面表现出卓越的表现.
- 与传统的MCTS相比,实现了显著更好的路径质量和更低的时间消耗,时间消耗减少了一半.
- SL-MCTS的性能与用于路径规划的专用代搜索算法相当.
结论:
- SL-MCTS提供了一种强大而高效的方法,用于单人解决问题,特别是在路径规划中.
- 光伏网络和自我学习的整合显著提高了MCTS的融合和效率.
- SL-MCTS提供了一个可扩展的解决方案,用于在复杂的决策任务中不断改进.
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