Hierarchical Reinforcement Learning for Temporal Pattern Prediction
Faith Johnson Kristin Dana
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.