Abstract: In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of ...

Reinforcement Learning - An Introduction, 2nd Edition, written by Richard S. Sutton and Andrew G. Barto, is kind of bible of reinforcement learning. It is a required reading for students and ...

The Dynamic Stochastic General Equilibrium (DSGE) model is a macroeconomic framework used to analyze the dynamics of economies over time in response to various shocks and policy changes. A Dynamic ...

Abstract: This paper proposes a dynamic programming algorithm for management of aircraft power supply system, which takes reliability as the optimization goal, and the power supply priority, the basic ...

The Smith-Waterman algorithm is a dynamic programming algorithm to find the optimal alignment of two sequences. This repository provides a basic implementation of the Smith-Waterman algorithm that has ...

Considering that the probability distribution of random variables in stochastic programming usually has incomplete information due to a perfect sample data in many real applications, this paper ...

China is once more tightening its grip on internet content, and this time algorithms are in the spotlight. The Cyberspace Administration of China has published upcoming rules that dictate how internet ...