In this work, we explore the use of hierarchical reinforcement learning (HRL) for the task of temporal sequence prediction. Using a combination of deep learning and HRL, we develop a stock agent to predict temporal price sequences from historical stock price data and a vehicle agent to predict steering angles from first person, dash cam images. Our results in both domains indicate that a type of HRL, called feudal reinforcement learning, provides significant improvements to training speed and stability and prediction accuracy over standard RL. A key component to this success is the multi-resolution structure that introduces both temporal and spatial abstraction into the network hierarchy.

我们探索了使用分层强化学习（HRL）来预测时间序列的任务。结合深度学习和分层强化学习的方法，我们开发了一个股票智能体来预测历史股价数据的时间序列，以及一个车辆智能体来预测第一人称车载摄像头图像中的转向角。我们在两个领域的结果表明，称为封建式强化学习的一种HRL方法，在训练速度、稳定性和预测准确性方面都有显著的改进。其中的关键因素是多分辨率结构，将时间和空间抽象引入了网络层次结构。

层次强化学习用于时间模式预测