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arXiv · 1507.04635

Black-Box Policy Search with Probabilistic Programs

Jan-Willem van de Meent·Brooks Paige·David Tolpin·Frank Wood
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Abstract

In this work, we explore how probabilistic programs can be used to represent policies in sequential decision problems. In this formulation, a probabilistic program is a black-box stochastic simulator for both the problem domain and the agent. We relate classic policy gradient techniques to recently introduced black-box variational methods which generalize to probabilistic program inference. We present case studies in the Canadian traveler problem, Rock Sample, and a benchmark for optimal diagnosis inspired by Guess Who. Each study illustrates how programs can efficiently represent policies using moderate numbers of parameters.

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Jan-Willem van de Meent, Brooks Paige, David Tolpin, Frank Wood. 2015-07-16. Black-Box Policy Search with Probabilistic Programs. https://arxiv.org/abs/1507.04635

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