Convolutional Neural Networks For Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control

Direct load control of a heterogeneous cluster of residential demand flexibility
sources is a high-dimensional control problem with partial observability. This work proposes a novel approach that uses a convolutional neural network to extract hidden state-time features to mitigate the curse of partial observability. More specific, a convolutional neural network is used as a function approximator to estimate the state-action value function or Q-function in the supervised learning step of fitted Qiteration.

Publication year:2016

BOF-publication weight:10

CSS-citation score:3

Authors:International

Authors from:Higher Education