政策之外的预测学习:对在线算法的实证研究
概括
这项研究比较了强化学习中的11个政策外预测算法,发现Emphatic TD (lambda) 的误差很低,但可能很慢. 新的Ratchet算法通过动态调整步骤大小来提高学习速度.
科学领域:
- 强化学习是一种强化学习.
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 政策之外的预测对于强化学习至关重要,它涉及从不同的政策产生的数据中学习价值函数.
- 现有的政策之外的预测算法面临着学习速度,参数灵敏度和非对称错误的挑战,特别是在高方差场景中.
- 重要抽样比率对算法性能的影响以及有效的步骤大小调整的需要仍然是关键的研究领域.
研究的目的:
- 实证地评估和比较11个不同的政策外预测学习算法的性能.
- 在各种任务中分析算法对参数的敏感性,学习速度和异常错误,包括具有高方差的任务.
- 提出新的步骤大小适应方法,即Ratchet算法,以提高表现最好的算法的学习速度.
主要方法:
- 在三个不同的任务上使用线性函数近似的11个政策外预测算法的实证比较:碰撞,房间和高方差房间.
- 基于对参数的灵敏度,学习速度和非对称错误的算法性能分析,重点关注由重要性抽样比率诱导的差异.
- 开发和提出两种新的步骤大小适应方法:步骤大小拉切和软步骤大小拉切.
主要成果:
- 算法性能受到重要抽样比率引入的方差的显著影响.
- 树备份,Vtrace和ABTD对高方差具有弹性,但对启动参数施加限制约束.
- 强烈的TD (lambda) 显示出较低的非对称误差,但学习速度可能较慢,而建议的Ratchet算法有效地提高了学习速度.
结论:
- 选择政策之外的预测算法对业绩产生了重大影响,特别是在高方差环境中.
- 拟议的Ratchet算法提供了一个有效的解决方案,通过动态调整步骤大小来加速学习.
- 进一步研究适应性步骤大小策略对于提高政策外预测方法的效率和适用性至关重要.
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