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Abstract:
We show that states of a dynamical system can be usefully
represented by multi-step, action-conditional predictions of
future observations. State representations that are grounded in
data in this way may be easier to learn, generalize better, and
be less dependent on accurate prior models than, for example,
POMDP state representations. Building on prior work by Jaeger and
by Rivest and Schapire, in this paper we compare and contrast a
linear specialization of the predictive approach with the state
representations used in POMDPs and in
k
-order Markov models. Ours is the first specific formulation of
the predictive idea that includes both stochasticity and actions
(controls). We show that any system has a linear predictive state
representation with number of predictions no greater than the
number of states in its minimal POMDP model.
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