扩大强化学习的视野,以探索好奇和创造性规划
Dale Zhou1,2, Aaron M Bornstein2,3
1Neurobiology and Behavior, 519 Biological Sciences Quad, University of California, Irvine, CA, USA dale.zhou@uci.eduhttps://dalezhou.com.
The Behavioral and brain sciences
|May 21, 2024
概括
好奇心和创造力平衡熟悉与新奇. 强化学习模型展示了信息搜索和生成如何影响规划视野,为这种权衡提供了解决方案.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 好奇心和创造力需要平衡利用已知的信息与探索新信息.
- 了解这种平衡对于解释复杂的认知行为和设计智能系统至关重要.
研究的目的:
- 以计算方式建模熟悉和新奇之间的权衡,寻求好奇心和创造力.
- 通过强化学习,正式捕获平衡勘探和开采的解决方案.
主要方法:
- 利用强化学习框架来分析决策过程.
- 根据它们对规划视界的影响,制定了信息搜索和生成的价值.
主要成果:
- 证明了信息搜索和生成对规划视野的互补效应可以正式捕捉熟悉和新奇之间的平衡.
- 确定了一系列解决方案,以在计算模型中实现这种平衡.
结论:
- 强化学习为理解好奇心和创造力背后的认知机制提供了一个强大的镜头.
- 拟议的框架提供了一种正式的方法来分析和潜在地优化智能代理的勘探和利用之间的平衡.
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