  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.

Machine Learning