提出基于序数回归的策略参数化方法，更好捕捉动作间的顺序关系。
Policy gradient methods for ordinal policies
- 用序数回归建模动作顺序，替代传统softmax参数化
- 在连续动作任务中表现媲美离散化策略，实测有效
- 适合有明确动作优先级的工业应用，如生产调度
在强化学习中，软最大值参数化是离散动作空间策略的标准方法，但无法捕捉动作之间的顺序关系。针对一个真实工业场景，我们提出一种基于序数回归模型的新型策略参数化方法，适用于强化学习设置。该方法解决实际挑战，数值实验表明其在真实应用和连续动作任务中均具有效性；通过将动作空间离散化并应用序数策略，可获得与现有方法竞争的性能。
原文摘要 · Abstract (English)
In reinforcement learning, the softmax parametrization is the standard approach for policies over discrete action spaces. However, it fails to capture the order relationship between actions. Motivated by a real-world industrial problem, we propose a novel policy parametrization based on ordinal regression models adapted to the reinforcement learning setting. Our approach addresses practical challenges, and numerical experiments demonstrate its effectiveness in real applications and in continuous action tasks, where discretizing the action space and applying the ordinal policy yields competitive performance.
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