适应性偏差差异权衡在演员关键算法的优势估计器中
Yurou Chen1, Fengyi Zhang1, Zhiyong Liu2
1The State Key Lab of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
概括
适应性优势估计方法通过动态平衡偏差和差异来改善连续控制任务. 这种方法优化了演员-关键性能,在机器人机动模拟中表现优于固定方法.
科学领域:
- 强化学习是一种强化学习.
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
背景情况:
- 在连续控制任务中,演员关键方法占据了突出地位.
- 优势估计器,演员批评中的关键批评者,混合状态值和样本回报.
- 在估计器中平衡偏差和差异是减少错误的关键,但最佳组合在训练期间会波动.
研究的目的:
- 探索偏差/偏差指标及其在优势估计中的最佳组合之间的关系.
- 为减少估计错误开发一种适应性优势估计器的一般形式.
- 与现有方法相比,评估拟议的自适应估计器的性能.
主要方法:
- 分析偏差和差异来源,使用先前对自适应优势估计 (AAE) 工作的指标.
- 数字实验用于研究指标与最佳组合策略之间的关系.
- 为状态值和样本回报开发一个通用的自适应组合形式.
主要成果:
- 建立了状态值和样本回报的自适应组合的一般形式.
- 证明这些自适应估计器可以达到较低的估计误差.
- 与通用优势估计器 (GAE) 相比,在模拟的机器人机动任务上实现了类似或更高的性能.
结论:
- 适应性优势估计提供了一种灵活的方法来优化演员-关键性能.
- 建议的方法在整个培训过程中有效地管理偏差差异权衡.
- 这项工作为更强大,更有效的连续控制算法提供了基础.
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