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We propose a framework for modeling and estimating the state of controlleddynamical systems, where an agent can affect the system through actions andreceives partial observations. Based on this framework, we propose thePredictive State Representation with Random Fourier Features (RFFPSR). A keyproperty in RFF-PSRs is that the state estimate is represented by a conditionaldistribution of future observations given future actions. RFF-PSRs combine thisrepresentation with moment-matching, kernel embedding and local optimization toachieve a method that enjoys several favorable qualities: It can representcontrolled environments which can be affected by actions; it has an efficientand theoretically justified learning algorithm; it uses a non-parametricrepresentation that has expressive power to represent continuous non-lineardynamics. We provide a detailed formulation, a theoretical analysis and anexperimental evaluation that demonstrates the effectiveness of our method.

An Efficient, Expressive and Local Minima-free Method for Learning Controlled Dynamical Systems