A Survey of Exploration Methods in Reinforcement Learning
It addresses the problem of understanding and organizing exploration strategies for researchers and practitioners in reinforcement learning, but it is incremental as it is a survey.
This paper surveys modern exploration methods in reinforcement learning, providing a taxonomy to categorize these techniques.
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.