分布政策梯度与分配价值函数
IEEE transactions on neural networks and learning systems
|April 26, 2024
概括
本研究引入了强化学习 (RL) 的分布政策梯度方法,增强探索并避免局部最佳. 新的方法,分发价值导向政策梯度 (DVDPG),在各种任务中表现优于传统方法.
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:
- 传统的强化学习 (RL) 算法估计了状态-动作对的预期回报.
- 分布式RL算法模型作为随机变量返回,估计对更丰富的环境信息的返回分布.
- 现有的研究已经广泛探索了以价值为基础的方法中的分布式RL,对政策梯度方法的应用有限.
研究的目的:
- 通过提出一种新的分配政策梯度方法来弥合研究差距.
- 通过结合分布式价值函数来增强政策梯度估计.
- 为了提高勘探效率,并在RL任务中逃避局部最佳情况.
主要方法:
- 提出了一个分布式政策梯度方法 (DVDPG),将分布式价值函数集成到政策梯度中.
- 估计了政策梯度的分布,而不仅仅是预期.
- 引入了两种政策梯度价值抽样机制:分布概率抽样和统一抽样.
主要成果:
- 拟议的DVDPG方法增强了政策梯度的随机性,改善了探索.
- 分布概率采样在稀疏奖励任务中表现优越.
- 这两种抽样机制在密集奖励任务中表现相似,传统方法被证明是特殊情况.
- 在OpenAI-gym任务上的实验结果证明了该方法的效率,表现优于基线.
结论:
- 分布政策梯度方法 (DVDPG) 与传统方法相比,具有显著的优势.
- 该方法有效地改善了各种奖励设置中的探索和政策优化.
- DVDPG代表了强化学习的宝贵进步,特别是对于政策梯度算法.
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