通过隐性变量分布增强采样器加强对OOD任务的政策概括
IEEE transactions on neural networks and learning systems
|October 30, 2025
概括
本研究引入了一种新的元强化学习方法 (LVDES),以改善分发以外任务的政策概括性. LVDES增强了任务表示和数据增强,大大提高了未见的目标的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 标准的强化学习因在政策培训中不充分考虑客观不确定性而难以处理分布外 (OOD) 任务.
- 现有的OOD泛化方法往往忽略潜伏任务表示中的结构信息,导致数据嵌入有偏见并影响政策泛化.
研究的目的:
- 提出一种基于上下文的元强化学习 (meta-RL) 方法,即隐性变量分布增强采样器 (LVDES),以提高对OOD任务的政策概括性.
- 为了提高任务表示空间的效率和对OOD场景的增强政策培训数据的准确性.
主要方法:
- LVDES包括四个模块:任务推断,用于结构化表示的任务分离 (TSM),用于数据增强的潜在增强 (LEM) 和政策模块.
- TSM学习了一个高度可分离的表示空间,而LEM则生成额外的任务轨迹来增强训练数据.
- 该方法利用高效的任务表示空间和增强的轨迹数据来增强探索和概括.
主要成果:
- 与MuJoCo和Meta-World基准的现有方法相比,LVDES在OOD任务的政策概括方面取得了显著的改进.
- 在OOD任务中,任务完成精度在使用LVDES时增加了60.20%.
- 与目前最有效的方法相比,平均勘探时间减少了62.99%.
结论:
- 拟议的LVDES方法通过改善任务表示和数据增强,有效地提高了对OOD任务的政策概括性.
- 在任务完成准确性和勘探效率方面,LVDES实现了卓越的性能,超过了当前最先进的方法.
- 这些发现强调了结构化的潜空间和准确的数据增强对于在未见的环境中强化学习的重要性.
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