Hindsight Credit Assignment
Anna Harutyunyan, Will Dabney, Thomas Mesnard, Mohammad Gheshlaghi Azar, Bilal Piot, Nicolas Heess, Hado P van Hasselt, Gregory Wayne
Abstract
We consider the problem of efficient credit assignment in reinforcement learning. In order to efficiently and meaningfully utilize new data, we propose to explicitly assign credit to past decisions based on the likelihood of them having led to the observed outcome. This approach uses new information in hindsight, rather than employing foresight. Somewhat surprisingly, we show that value functions can be rewritten through this lens, yielding a new family of algorithms. We study the properties of these algorithms, and empirically show that they successfully address important credit assignment challenges, through a set of illustrative tasks.
BibTeX
@inproceedings{NEURIPS2019_195f1538,
author = {Harutyunyan, Anna and Dabney, Will and Mesnard, Thomas and Gheshlaghi Azar, Mohammad and Piot, Bilal and Heess, Nicolas and van Hasselt, Hado P and Wayne, Gregory and Singh, Satinder and Precup, Doina and Munos, Remi},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Hindsight Credit Assignment},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/195f15384c2a79cedf293e4a847ce85c-Paper.pdf},
volume = {32},
year = {2019}
}