Abstract: In this paper, a new data-based Q-learning algorithm is proposed to address the optimal control issue for a class of discrete-time switched affine systems (SASs). The algorithm shifts the ...

Implemented Behavior Cloning, DAgger, Double Q-Learning, Dueling DQN, and Proximal Policy Optimization (PPO) in a simulated environment and analyzed/compared their performance in terms of efficiency, ...

Abstract: This paper focuses on solving the linear quadratic regulator problem for discrete-time linear systems without knowing system matrices. The classical Q-learning methods for linear systems can ...

Add Decrypt as your preferred source to see more of our stories on Google. It was a corporate espionage story even a real human screenwriter couldn’t have dreamed up. OpenAI, which sparked the global ...

When beginning to study reinforcement learning, temporal difference learning is frequently used as an entry point. In order to elaborate on this concept and demonstrate the fundamentals of ...

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 to the ...

This README explains how to use the code for the paper written Calvano et al. (2019). The code simulates experiments from the paper using FORTRAN, with outputs processed in R/RStudio to generate ...