使用临时分层架构优化注意力和认知控制成本
Devdhar Patel1, Terrence Sejnowski2,3, Hava Siegelmann4
1Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA 01003, U.S.A. devdharpatel@gmail.com.
Neural computation
|October 9, 2024
概括
本研究介绍了一种生物启发的临时层架构 (TLA),用于强化学习. TLA优化了性能和计算效率,在决策环境中优于现有方法.
科学领域:
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 目前的强化学习 (RL) 优先考虑的是性能而不是效率.
- 生物系统通过优化能量和决策来实现高性能.
- RL 代理通常缺乏对计算资源的限制.
研究的目的:
- 开发一个包含计算约束的RL框架.
- 研究生物启发的机制,以实现高效的决策.
- 为了在有限的能源和时间预算下提高RL剂的性能.
主要方法:
- 提出了一个决策有限的马尔科夫决策过程 (DB-MDP),以限制决策和能源.
- 引入了一个具有不同的时间尺度和能源需求的临时分层架构 (TLA).
- 在DB-MDP和连续控制环境中与现有的RL算法和新型TLA进行了实验.
主要成果:
- 现有的RL算法在决策环境中表现不佳或低于最佳.
- 在决策限制和持续控制任务中,TLA实现了最佳性能.
- TLA与最先进的性能相匹配,同时显著降低了计算成本.
结论:
- 在资源有限的环境中,TLA可实现高效和高性能RL代理.
- DB-MDP框架为能源意识的RL提供了一个基准.
- 这项研究为节能和时间意识的人工智能铺平了道路.
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