Reinforcement Learning Applications

TL;DR

This paper provides an overview of reinforcement learning, highlighting its successful applications across various domains and discussing fundamental concepts, issues, and future outlooks.

Contribution

It offers a comprehensive survey of RL applications in multiple fields, summarizing key successes, challenges, and practical considerations.

Findings

RL has been successfully applied in recommender systems and robotics.

Key issues include sample efficiency and safety in RL applications.

The outlook emphasizes future research directions in RL deployment.

Abstract

We start with a brief introduction to reinforcement learning (RL), about its successful stories, basics, an example, issues, the ICML 2019 Workshop on RL for Real Life, how to use it, study material and an outlook. Then we discuss a selection of RL applications, including recommender systems, computer systems, energy, finance, healthcare, robotics, and transportation.