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自由能量投影模拟 (FEPS):具有可解释性的主动推断

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概括
此摘要是机器生成的。

我们介绍自由能量投影模拟 (FEPS), 通过基于预测准确性的观察,FEPS代理有效地解决环境模两可,并推断出最佳政策.

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科学领域:

  • 计算神经科学
  • 人工智能
  • 复杂的系统

背景情况:

  • 自由能量原理 (FEP) 和主动推理 (AIF) 为理解自组织系统中的学习,认知,感知和行动提供了统一的框架.
  • 强化学习 (RL) 代理经常使用深度神经网络,已被开发用于执行主动推理任务,最近的努力集中在提高复杂环境中的性能.

研究的目的:

  • 在FEP和AIF的约束范围内开发可解释的代理模型方法,避免深度神经网络.
  • 引入自由能源投影模拟 (FEPS) 作为一个用于代理模型和政策优化的新方法.

主要方法:

  • FEPS代理使用内部奖励来构建部分可观测环境的世界模型.
  • 政策是通过将预期的自由能量降到最低,这是AIF的核心原则.
  • 包括管理长期目标和减轻隐藏状态估计预测错误的技术.

主要成果:

  • 在两种RL环境 (定时响应和部分可观测导航) 中,FEPS代理成功地解决了模糊性,其灵感来源于行为生物学.
  • 代理人表现出仅基于预测准确性的观察情况的能力.
  • 对于环境中的各种目标观测,可以灵活推断最佳政策.

结论:

  • FEPS为FEP和AIF中的建模代理提供了深度神经网络的可解释替代方案.
  • 该模型有效地处理环境模糊性,并通过基于预测准确性的上下文化优化政策.
  • 在复杂的,部分可观测的环境中开发适应性和目标导向的代理是有前途的.