可行的政策代与保证安全的勘探
IEEE transactions on cybernetics
|March 18, 2025
概括
本研究介绍了一种安全的强化学习 (RL) 框架,保证在现实训练过程中没有违反约束的情况. 我们的可行政策代方法通过仅在定义的可行区域内进行探索来确保绝对的安全.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 机器学习和控制系统
背景情况:
- 安全的强化学习 (RL) 对现实应用来说至关重要,以防止损害和风险.
- 现有的方法往往会在训练期间危及安全,或者在最佳后解决安全问题.
研究的目的:
- 提出一个可行的政策代框架,以确保在线RL探索期间的绝对安全.
- 为了确保在现实世界的互动中永远不会发生约束违规行为.
主要方法:
- 在每个阶段将环境勘探限制在动态定义的可行区域内.
- 使用一种新的约束衰变函数,对前进不变的不确定性.
- 开发具有演员-关键-场景架构的实用算法 (安全探索,模型错误估计,网络更新).
主要成果:
- 在训练过程中实现了与基线可比的性能,并且在训练期间没有违反约束.
- 证明了可行的地区和政策改进的单调扩张.
- 与基线算法相比,对于类似的性能需要大量的违规行为.
结论:
- 拟议的框架保证在线RL探索的绝对安全.
- 可行的政策代显示出在复杂的现实世界系统中安全部署的巨大潜力.
- 允许复杂系统在线演变,而不会影响安全.
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