Abstract: Function approximation has experienced significant success in the field of reinforcement learning (RL). Despite a handful of progress on developing theory for nonstationary RL with function ...

Abstract: We propose applying the reaction-diffusion equation on a graph to the function approximation for reinforcement learning. which realizes adaptive resolution according to the complexity of the ...

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 ...

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 ...

@Article{Tsitsiklis+VanRoy:1997, author = "Tsitsiklis, John N. and Van Roy, Benjamin", title = "An analysis of temporal-difference learning with function ...

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 ...

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

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 ...

Resistant training in radial basis function (RBF) networks is the topic of this paper. In this paper, one modification of Gauss-Newton training algorithm based on the theory of robust regression for ...

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 ...