Abstract: Reinforcement Learning is a branch of machine learning to learn control strategies that achieve a given objective through trial-and-error in the environment ...

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 ...

@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 ...

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 ...

@InProceedings{Baird:1995, author = "Baird, Leemon", title = "Residual Algorithms: Reinforcement Learning with Function Approximation", booktitle = "Proceedings of ...

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 ...