Recent years have seen the development of effi- cient and provably correct spectral algorithms for learning models of partially observable environ-.
Jul 9, 2016 · In this paper we introduce the multi-step predictive state representation (M-PSR) and an associated learning algorithm that finds and leverages ...
Of course, other multi-step prediction error gradient algorithms are possible; our learning algorithm ignores the effect of changing the parameters on the input.
We show that states of a dynamical system can be usefully repre- sented by multi-step, action-conditional predictions of future ob- servations.
Feb 14, 2017 · Over the past decade there has been considerable interest in spectral algorithms for learning Pre- dictive State Representations (PSRs).
We show that states of a dynamical system can be usefully represented by multi-step, action-conditional predictions of future observations. State ...
We show that states of a dynamical system can be usefully represented by multi-step, action-conditional predictions of future observations. View full-text.
We believe that learning from multi-step predictions may be more robust and efficient. At the same time, other multi-step prediction error algorithms are ...
Predictive state representations (PSRs) are a method of modeling dynam- ical systems using only observable data, such as actions and observations,.
Predictive state representations (PSRs) are a recently-developed way to model discrete- time, controlled dynamical systems. We.
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