A Survey of Exploration Methods in Reinforcement Learning
Machine Learning
2021-09-03 v2 Artificial Intelligence
Abstract
Exploration is an essential component of reinforcement learning algorithms, where agents need to learn how to predict and control unknown and often stochastic environments. Reinforcement learning agents depend crucially on exploration to obtain informative data for the learning process as the lack of enough information could hinder effective learning. In this article, we provide a survey of modern exploration methods in (Sequential) reinforcement learning, as well as a taxonomy of exploration methods.
Cite
@article{arxiv.2109.00157,
title = {A Survey of Exploration Methods in Reinforcement Learning},
author = {Susan Amin and Maziar Gomrokchi and Harsh Satija and Herke van Hoof and Doina Precup},
journal= {arXiv preprint arXiv:2109.00157},
year = {2021}
}