非OAB:非政策政策梯度方法与最佳行动依赖基线.
概括
本研究引入了一种非政策政策梯度方法,具有最佳的取决于行动的基线 (Off-OAB),以减少强化学习培训的差异. 外OAB提高了采样效率,并在基准任务上优于现有方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 基于政策的方法在强化学习 (RL) 中取得了成功.
- 非政策政策梯度 (OPPG) 方法利用非政策数据,但存在很高的波动.
- 高差异导致训练中的样本效率低下.
研究的目的:
- 提出一种新的非政策政策梯度方法来缓解差异.
- 引入一个最佳的取决于行动的基线 (Off-OAB),以进行公正和低变异的OPPG估计.
- 通过近似的最佳基线来提高计算效率.
主要方法:
- 为OPPG.开发了一个最佳的取决于行动的基线 (Off-OAB).
- 理论上将差异最小化,同时保持不偏见.
- 为实际效率设计了最佳基线的近似版本.
主要成果:
- 在Off-OAB方法明显减少了OPPG估计器的差异.
- 在6个OpenAI Gym和MuJoCo任务中进行评估.
- 在大多数任务中超越了最先进的方法.
结论:
- 拟议的Off-OAB方法有效地减少了政策外政策梯度估计的差异.
- 在Off-OAB增强了样本效率和在具有挑战性的RL任务中的性能.
- 大致的基线确保了实际的计算效率.
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