用单模式概率分布分离连续行动空间,用于政策上的强化学习
IEEE transactions on neural networks and learning systems
|August 27, 2024
概括
在强化学习 (RL) 中分离连续行动空间可以增加差异. 本研究介绍了一种使用Poisson分布的单模政策,以提高复杂控制任务中的稳定性和性能.
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:
- 在政策强化学习 (RL) 中分离连续行动空间简化了优化,但由于忽视了行动顺序,可能导致高差异.
- 在不考虑其固有的顺序的情况下,离散行动的爆炸可能会对政策梯度 (PG) 估计器的性能产生负面影响.
研究的目的:
- 引入对政策上RL的新架构,限制离散政策成为单模式.
- 通过明确的单模式概率分布,利用潜在的连续动作空间的连续性.
- 减少政策梯度估计器的差异,提高学习稳定性.
主要方法:
- 使用波桑概率分布实现单模分立政策架构.
- 将政策限制为单模式,以更好地利用行动空间的连续性.
- 在具有挑战性的控制任务上进行广泛的实验,包括人形任务.
主要成果:
- 与标准方法相比,单模式离散政策实现了明显更快的趋同.
- 在复杂的政策性RL任务中表现更高,特别是在具有高度挑战性的场景中,如Humanoid.
- 理论分析证实,在拟议的单模式政策下,PG估计器的差异较小.
结论:
- 拟议的单模式离散政策架构有效地解决了与在政策RL中的行动空间离散相关的差异问题.
- 这种方法提高了学习稳定性和性能,特别是在复杂的机器人控制任务中.
- 单模式概率分布的明确使用更好地利用了行动空间的连续性,以改善RL结果.
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