A Crash Course on Reinforcement Learning
Machine Learning
2021-03-09 v1 Systems and Control
Systems and Control
Abstract
The emerging field of Reinforcement Learning (RL) has led to impressive results in varied domains like strategy games, robotics, etc. This handout aims to give a simple introduction to RL from control perspective and discuss three possible approaches to solve an RL problem: Policy Gradient, Policy Iteration, and Model-building. Dynamical systems might have discrete action-space like cartpole where two possible actions are +1 and -1 or continuous action space like linear Gaussian systems. Our discussion covers both cases.
Keywords
Cite
@article{arxiv.2103.04910,
title = {A Crash Course on Reinforcement Learning},
author = {Farnaz Adib Yaghmaie and Lennart Ljung},
journal= {arXiv preprint arXiv:2103.04910},
year = {2021}
}
Comments
https://github.com/FarnazAdib/Crash_course_on_RL