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重新思考折扣规范化:新的解释,意想不到的后果和强化学习规范化解决方案
Sarah Rathnam1, Sonali Parbhoo2, Siddharth Swaroop1
1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138 USA.
概括
在强化学习中,折扣规范化可能会导致由于数据不均而导致表现不佳. 本研究引入了新的,国家具体行动的方法,通过解决这些意想不到的后果来改善政策优化.
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:
- 折扣规范化是强化学习 (RL) 中常见的一种技术,以防止过度匹配,特别是在稀疏或杂的数据中.
- 它通常被理解为减少政策优化中延迟奖励或未来状态的重要性.
- 现有的方法采用全局折扣因子,这可能不适合所有状态-动作对.
研究的目的:
- 揭示对折扣正规化的替代解释,突出其意想不到的后果.
- 激励开发解决这些局限性的新型规范化技术.
- 提出和验证国家特定的规范化方法,将折扣规范化概括起来.
主要方法:
- 证明了在基于模型和无模型的RL中对折扣正规化的两个替代理论观点.
- 展示折扣正规化如何作为先前有利于国家行动对,在基于模型的RL中提供更多的过渡数据.
- 显示折扣正规化的相当于在没有模型的RL中加权平均贝尔曼更新.
主要成果:
- 当从不均分布的数据中估计过渡矩阵时,折扣规范化可能导致性能差.
- 拟议的州具体行动方法通过局部调整参数来概括折扣规范化.
- 在表式和连续状态空间的实证验证表明了新方法的有效性.
结论:
- 折扣规范化会产生意想不到的后果,特别是在国家行动对之间数据分布的变化.
- 国家具体行动规范化提供了一个更强大,更适应性的方法来优化RL的政策.
- 这些新的方法为标准折扣规范化的失败提供了补救措施,在各种RL环境中提高了性能.
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