Least Squares Solutions of the HJB Equation With Neural Network Value-Function Approximators

Universal Value Function Approximators

Value functions are a core component of reinforcement learning systems. The main idea is to to construct a single function approximator V (s; θ) that estimates the long-term reward from any state s, using parameters θ. In this paper we introduce universal value function approximators (UVFAs) V (s, g; θ) that generalise not just over states s but also over goals g. We develop an efficient techni...

Least Squares Solutions of Inconsistent Fuzzy Linear Matrix Equations

Nonnegative Least Squares Learning for the Random Neural Network

In this paper, a novel supervised batch learning algorithm for the Random Neural Network (RNN) is proposed. The RNN equations associated with training are purposively approximated to obtain a linear Nonnegative Least Squares (NNLS) problem that is strictly convex and can be solved to optimality. Following a review of selected algorithms, a simple and efficient approach is employed after being i...

Least-Squares Method for the Oseen Equation

This article studies the least-squares finite element method for the linearized, stationary Navier–Stokes equation based on the stress-velocity-pressure formulation in d dimensions (d = 2 or 3). The least-squares functional is simply defined as the sum of the squares of the L2 norm of the residuals. It is shown that the homogeneous least-squares functional is elliptic and continuous in the H(di...

application of artificial neural network and ordinary least squares regression in modeling land use changes

owing to the vital effects of future land use changes, it is necessary to predict land use growth pattern before any decision making by the authorities and decision makers. purpose of this research is to model land use change of kohmare scorch plain of shiraz province using ordinary least squares regression (ols) for pre-processing variables and modeling using neural networks. to perform this m...