Reinforcement Learning: A Survey

TL;DR

This survey provides a comprehensive overview of reinforcement learning, covering its history, core concepts, challenges, and practical applications, aimed at researchers with a machine learning background.

Contribution

It offers a broad, accessible summary of reinforcement learning's theoretical foundations, key issues, and recent developments, highlighting its practical utility and open research questions.

Findings

Reinforcement learning involves trial-and-error interactions with dynamic environments.

Key issues include exploration-exploitation trade-offs and learning from delayed rewards.

Current methods have practical utility but face challenges like hidden states and scalability.

Abstract

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.