In recent years, neural networks have once again triggered an increased interest among researchers in the machine learning community. So-called deep neural networks model functions using a composition ...

Abstract: We develop an approach for the optimization of continuous costly functions that uses a space-filling experimental design and local function approximation to reduce the number of function ...

In this series, we will continue to explore reinforcement learning (RL) concepts guided by the great textbook Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto. Summary ...

Abstract: Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of ...

Traditional tabular implementations in reinforcement learning often face limitations in handling large state or action spaces. Enter function approximation, a paradigm that overcomes these constraints ...

Function Approximation and Classification implementations using Neural Network Toolbox in MATLAB. Function Approximation was done on California Housing data-set and Classification was done on SPAM ...

This project involves approximating a function to solve an optimization problem. Functions can often be costly to write in code. Approximating a function can sometimes save time and money. Especially ...

Reinforcement learning is a body of theory and techniques for optimal sequential decision making developed in the last thirty years primarily within the machine learning and operations research ...

Approximation methods in function spaces characterise how well complex functions can be represented or recovered using limited information such as function values or linear measurements. Central to ...

ABSTRACT: Q-learning is a popular temporal-difference reinforcement learning algorithm which often explicitly stores state values using lookup tables. This implementation has been proven to converge ...