Closing the Learning-Planning Loop with Predictive State Representations
Abstract
A central problem in artificial intelligence is that of planning to maximize future reward under uncertainty in a partially observable environment. In this paper we propose and demonstrate a novel algorithm which accurately learns a model of such an environment directly from sequences of action-observation pairs. We then close the loop from observations to actions by planning in the learned model and recovering a policy which is near-optimal in the original environment. Specifically, we present an efficient and statistically consistent spectral algorithm for learning the parameters of a Predictive State Representation (PSR). We demonstrate the algorithm by learning a model of a simulated high-dimensional, vision-based mobile robot planning task, and then perform approximate point-based planning in the learned PSR. Analysis of our results shows that the algorithm learns a state space which efficiently captures the essential features of the environment. This representation allows accurate prediction with a small number of parameters, and enables successful and efficient planning.
Cite
@article{arxiv.0912.2385,
title = {Closing the Learning-Planning Loop with Predictive State Representations},
author = {Byron Boots and Sajid M. Siddiqi and Geoffrey J. Gordon},
journal= {arXiv preprint arXiv:0912.2385},
year = {2009}
}