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

Understand

We propose a framework for modeling and estimating the state of controlled dynamical systems, where an agent can affect the system through actions and receives partial observations.

• Based on this framework, we propose the Predictive State Representation with Random Fourier Features (RFFPSR).
• A key property in RFF-PSRs is that the state estimate is represented by a conditional distribution of future observations given future actions.
• RFF-PSRs combine this representation with moment-matching, kernel embedding and local optimization to achieve a method that enjoys several favorable qualities: It can represent controlled environments which can be affected by actions; it has an efficient and theoretically justified learning algorithm; it uses a non-parametric representation that has expressive power to represent continuous non-linear dynamics.