双连续过度放松的Q学习,扩展到深度强化学习
概括
我们引入了一种新的强化学习算法,即无模型的双连续过度放松的Q学习 (MF-DSORQL),以解决Q学习中的缓慢融合和偏差问题. 与现有方法相比,这种改进的算法表明偏差减少.
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:
- Q-learning (QL) 是一个基本的强化学习 (RL) 算法,但它的融合可能很慢,特别是在接近1的折扣因子时.
- 连续过度放松的QLL (SORQL) 加快了融合,但在表格设置中遭受了模型依赖和高估偏差.
研究的目的:
- 提出一种新的基于样本的,无模型的双SORQL (MF-DSORQL) 算法,以克服SORQL的局限性.
- 从理论和经验上评估MF-DSORQL的偏差和收性质.
主要方法:
- 开发了MF-DSORQL算法,一种基于样本的,没有模型的方法.
- 在受限性假设下的表格设置的理论收分析.
- 将MF-DSORQL扩展到使用深度强化学习 (DRL) 的大规模问题.
主要成果:
- 与SORQL相比,MF-DSORQL在理论上和经验上表现出较少的偏差.
- 提供了表格式MF-DSORQL的收分析.
- MF-DSORQL 的 DRL 扩展是根据基准问题进行验证的.
结论:
- 在强化学习中,MF-DSORQL为SORQL的局限性提供了有效的解决方案.
- 该算法适用于表式和大规模的深度强化学习应用.
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