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贝叶斯的嗅觉搜索在动荡的流动中的最佳政策
R A Heinonen1, L Biferale1, A Celani2
1Department of Physics and INFN, University of Rome "Tor Vergata," Via della Ricerca Scientifica 1, 00133 Rome, Italy.
Physical review. E
|June 17, 2023
概括
飞行昆虫使用复杂的策略,在强风的条件下找到气味来源. 使用Perseus算法计算的新近最佳策略显著优于嗅觉跟踪的启发式方法.
科学领域:
- 计算生物学 计算生物学
- 动物行为 动物行为
- 机器人技术 机器人技术 机器人技术
背景情况:
- 昆虫经常在动荡的环境中寻找气味来源.
- 风驱动的向产生间歇性的气味斑,挑战梯度登策略.
- 由于基因度低且波动,传统的化疗是无效的.
研究的目的:
- 开发和评估近乎最佳的搜索策略,用于昆虫定位空中线索.
- 为了比较一个新的算法的性能与既定的启发式方法.
- 在模拟环境中分析搜索难度和政策稳定性.
主要方法:
- 制定昆虫搜索作为一个部分可观测的马尔科夫决策过程 (POMDP).
- 使用Perseus算法来计算接近最佳的策略.
- 在2D网格上模拟搜索策略并比较到达时间统计.
主要成果:
- 来自Perseus的策略在启发式策略 (infotaxis,Thompson采样,QMDP) 上表现优越.
- 基于最初的昆虫位置,分析了搜索难度.
- 评估了政策对环境变化的稳定性和最初的信念选择.
结论:
- 通过Perseus计算的近最佳策略为间歇性提示搜索提供了显著的优势.
- 这项研究提供了关于有效的策略,以导航动荡,充满气味的环境的见解.
- 提供了关于Perseus算法实现的详细讨论,包括奖励塑造.
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