在多目标强化学习的框架内,将方法-避免冲突置于多目标强化学习的框架内
Enkhzaya Enkhtaivan1, Joel Nishimura2, Amy Cochran3,4
1Department of Mathematics, University of Wisconsin, 480 Lincoln Drive, Madison, 53706, WI, USA.
Bulletin of mathematical biology
|October 14, 2023
概括
本研究重构了使用多目标多武装强盗框架来处理避免冲突的任务. 提出了新的任务设计,以更好地区分精神病学研究中的不同决策模型.
科学领域:
- 神经科学是一个神经科学.
- 计算精神病学是一种计算精神病学.
- 强化学习是一种强化学习.
背景情况:
- 精神疾病往往涉及到决策能力受损,特别是在方法避免冲突期间.
- 当前的实验任务将奖励和伤害结合起来,在强盗任务范式中创建这些冲突.
研究的目的:
- 将避免冲突的任务重新定义为一个多目标的多武装强盗问题.
- 通过长期平均结果来定义决策者,将决策过程与学习脱而出.
- 开发能够区分各种决策模式的新任务设计.
主要方法:
- 作为一个多重目标的多重武装的强盗,框架方法-避免冲突任务.
- 将决策者定义为限制行动序列,以将决策与学习分开.
- 通过他们的"可行区域"来描述决策者 - - 所有可能的长期业绩的集合.
- 分析基于强化学习模型的三个决策者的例子.
主要成果:
- 现有的任务不足以区分基于强化学习的决策者.
- 决策者的可行区域的几何属性可用于差异化.
- 提出了新的任务设计,其几何结构量身定制,以更好地区分模型.
结论:
- 拟议的多目标多武装强盗框架为接近-避免冲突任务提供了一个新的视角.
- 当前的实验范式可能无法充分捕捉精神疾病中决策缺陷的细微差别.
- 未来的研究应该利用精细的任务设计来改善决策行为的建模和理解.
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