虚假的相关性减少离线强化学习强化学习
IEEE transactions on pattern analysis and machine intelligence
|October 30, 2023
概括
本研究为线下强化学习 (RL) 引入了falSe相关性减少 (SCORE),以解决不确定性和决策之间的错误相关性. 通过使用化行为克隆调节器,SCORE提高了性能,并加速了融合.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 强化学习是一种强化学习.
背景情况:
- 线下强化学习 (RL) 使用大型数据集进行顺序决策.
- 现有的方法主要侧重于分布外 (OOD) 行动,忽视不确定性驱动的次优化.
研究的目的:
- 解决认识不确定性和离线RL决策之间的错误相关性这一关键问题.
- 提出一种新的算法,falSe 相关性减少 (SCORE),以提高线下RL的性能和可靠性.
主要方法:
- SCORE使用一个化行为克隆调节器来改进不确定性估计.
- 这种规范化是缓解虚假相关性引起的次优度的关键.
主要成果:
- 在标准线下RL基准 (D4RL) 上,SCORE实现了最先进的 (SoTA) 性能.
- 经验结果表明,任务完成速度加快了3.1倍.
- 理论分析验证了算法的趋同到一个最佳的政策.
结论:
- SCORE有效地减少了线下RL中的错误相关性,从而改善了决策.
- 该算法既提供了实际有效性,也提供了对趋同的理论保证.
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