Closing the Learning-Planning Loop with Predictive State Representations

TL;DR

This paper introduces a spectral algorithm for learning Predictive State Representations from action-observation sequences, enabling effective planning in partially observable environments, demonstrated on a vision-based robot task.

Contribution

It presents a novel, efficient, and statistically consistent spectral method for learning PSRs directly from data, facilitating near-optimal planning in complex environments.

Findings

The algorithm accurately learns environment models from sequences.

Learned models capture essential environment features efficiently.

Planning in learned PSRs yields successful task performance.

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.