Streaming Linear System Identification with Reverse Experience Replay

We consider the problem of estimating a stochastic linear time-invariant (LTI) dynamical system from a single trajectory via streaming algorithms. The problem is equivalent to estimating the parameters of vector auto-regressive ($\var$) models encountered in time series analysis (\cite{hamilton2020time}). A recent sequence of papers \citep{faradonbeh2018finite,simchowitz2018learning,sarkar2019near} show that ordinary least squares (OLS) regression can be used to provide optimal finite time estimator for the problem. However, such techniques apply for {\em offline} setting where the optimal solution of OLS is available {\em apriori}. But, in many problems of interest as encountered in reinforcement learning (RL), it is important to estimate the parameters on the go using gradient oracle. This task is challenging since standard methods like SGD might not perform well when using stochastic gradients from correlated data points \citep{gyorfi1996averaged,nagaraj2020least}. In this work, we propose a novel algorithm, SGD with Reverse Experience Replay ($\sgdber$), that is inspired by the experience replay (ER) technique popular in the RL literature \citep{lin1992self}. $\sgdber$ divides data into small buffers and runs SGD backwards on the data stored in the individual buffers. We show that this algorithm exactly deconstructs the dependency structure and obtains information theoretically optimal guarantees for both parameter error and prediction error for standard problem settings. Thus, we provide the first -- to the best of our knowledge -- optimal SGD-style algorithm for the classical problem of linear system identification aka $\var$ model estimation. Our work demonstrates that knowledge of dependency structure can aid us in designing algorithms which can deconstruct the dependencies between samples optimally in an online fashion.

• Machine Intelligence