  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.
