Predictive linear-Gaussian models of controlled stochastic dynamical systems

We introduce the controlled predictive linearGaussian model (cPLG), a model that uses predictive state to model discrete-time dynamical systems with real-valued observations and vector-valued actions. This extends the PLG, an uncontrolled model recently introduced by Rudary et al. (2005). We show that the cPLG subsumes controlled linear dynamical systems (LDS, also called Kalman filter models) of equal dimension, but requires fewer parameters. We also introduce the predictive linearquadratic Gaussian problem, a cost-minimization problem based on the cPLG that we show is equivalent to linear-quadratic Gaussian problems (LQG, sometimes called LQR). We present an algorithm to estimate cPLG parameters from data, and show that our algorithm is a consistent estimation procedure. Finally, we present empirical results suggesting that our algorithm performs favorably compared to expectation maximization on controlled LDS models.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: Matthew R. Rudary, Satinder P. Singh
• Keywords: Controlled Lds Models, Discrete-time Dynamical Systems, ICML 2006, Machine Learning, Predictive LinearGaussian Model