A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges

TL;DR

This survey provides an extensive overview of reinforcement learning algorithms, analyzing their strengths, weaknesses, and practical challenges to guide researchers and practitioners in applying RL effectively.

Contribution

It offers a detailed categorization and evaluation of RL algorithms from basic to advanced techniques, including practical insights for real-world applications.

Findings

Comparison of algorithms based on scalability and sample efficiency

Identification of key challenges like convergence and exploration-exploitation trade-offs

Guidelines for selecting suitable RL methods for different scenarios

Abstract

Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence (AI), enabling agents to learn optimal behaviors through interactions with their environments. Drawing from the foundations of trial and error, RL equips agents to make informed decisions through feedback in the form of rewards or penalties. This paper presents a comprehensive survey of RL, meticulously analyzing a wide range of algorithms, from foundational tabular methods to advanced Deep Reinforcement Learning (DRL) techniques. We categorize and evaluate these algorithms based on key criteria such as scalability, sample efficiency, and suitability. We compare the methods in the form of their strengths and weaknesses in diverse settings. Additionally, we offer practical insights into the selection and implementation of RL algorithms, addressing common challenges like convergence, stability, and the exploration-exploitation dilemma. This paper serves as a comprehensive reference for researchers and practitioners aiming to harness the full potential of RL in solving complex, real-world problems.