@inproceedings{812147e0e14c469798bbc689aa927236,
title = "Feature reinforcement learning: State of the art",
abstract = "Feature reinforcement learning was introduced five years ago as a principled and practical approach to history-based learning. This paper examines the progress since its inception. We now have both model-based and model-free cost functions, most recently extended to the function approximation setting. Our current work is geared towards playing ATARI games using imitation learning, where we use Feature RL as a feature selection method for high-dimensional domains.",
author = "Mayank Daswani and Peter Sunehag and Marcus Hutter",
note = "Publisher Copyright: {\textcopyright} Copyright 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 28th AAAI Conference on Artificial Intelligence, AAAI 2014 ; Conference date: 28-07-2014",
year = "2014",
language = "English",
series = "AAAI Workshop - Technical Report",
publisher = "AI Access Foundation",
pages = "2--5",
booktitle = "Sequential Decision-Making with Big Data - Papers Presented at the 28th AAAI Conference on Artificial Intelligence, Technical Report",
}