AISTATS 2016poster31 citations
Black-Box Policy Search with Probabilistic Programs
Jan-Willem Vandemeent, Brooks Paige, David Tolpin, Frank Wood
Abstract
In this work we show how to represent policies as programs: that is, as stochastic simulators with tunable parameters. To learn the parameters of such policies we develop connections between black box variational inference and existing policy search approaches. We then explain how such learning can be implemented in a probabilistic programming system. Using our own novel implementation of such a system we demonstrate both conciseness of policy representation and automatic policy parameter learning for a set of canonical reinforcement learning problems.
BibTeX
@InProceedings{pmlr-v51-vandemeent16,
title = {Black-Box Policy Search with Probabilistic Programs},
author = {Vandemeent, Jan-Willem and Paige, Brooks and Tolpin, David and Wood, Frank},
booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
pages = {1195--1204},
year = {2016},
editor = {Gretton, Arthur and Robert, Christian C.},
volume = {51},
series = {Proceedings of Machine Learning Research},
address = {Cadiz, Spain},
month = {09--11 May},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v51/vandemeent16.pdf},
url = {https://proceedings.mlr.press/v51/vandemeent16.html},
abstract = {In this work we show how to represent policies as programs: that is, as stochastic simulators with tunable parameters. To learn the parameters of such policies we develop connections between black box variational inference and existing policy search approaches. We then explain how such learning can be implemented in a probabilistic programming system. Using our own novel implementation of such a system we demonstrate both conciseness of policy representation and automatic policy parameter learning for a set of canonical reinforcement learning problems.}
}