Hierarchical Reinforcement Learning for Temporal Pattern Prediction

Faith Johnson, Kristin Dana
10/9/2023
cs.LG

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Abstract

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.

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Cite this paper

@article{johnson2023hierarchical,
  title  = {Hierarchical Reinforcement Learning for Temporal Pattern Prediction},
  author = {Faith Johnson and Kristin Dana},
  year   = {2023},
  eprint = {2310.05695},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.05695v1}
}

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