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arXiv · 0912.2385

Closing the Learning-Planning Loop with Predictive State Representations

Byron Boots·Sajid M. Siddiqi·Geoffrey J. Gordon
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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.

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Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon. 2009-12-12. Closing the Learning-Planning Loop with Predictive State Representations. https://arxiv.org/abs/0912.2385

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