A Survey of Exploration Methods in Reinforcement Learning

TL;DR

This survey reviews modern exploration techniques in reinforcement learning, categorizing methods to help understand how agents efficiently gather information in complex, stochastic environments.

Contribution

It provides a comprehensive taxonomy and overview of recent exploration strategies in reinforcement learning, highlighting their differences and applications.

Findings

Classifies exploration methods into distinct categories

Summarizes recent advances in exploration strategies

Identifies gaps and future directions in exploration research

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.