Introduction to Reinforcement Learning

TL;DR

This paper offers a comprehensive overview of Reinforcement Learning, explaining core concepts, algorithms, and resources to help beginners understand how agents learn to make decisions through interaction with their environment.

Contribution

It provides a structured, beginner-friendly introduction to RL, covering fundamental components, various algorithms, and learning resources, which simplifies complex concepts for new learners.

Findings

Clarifies core RL concepts and components

Categorizes key RL algorithms and methodologies

Provides curated resources for further learning

Abstract

Reinforcement Learning (RL), a subfield of Artificial Intelligence (AI), focuses on training agents to make decisions by interacting with their environment to maximize cumulative rewards. This paper provides an overview of RL, covering its core concepts, methodologies, and resources for further learning. It offers a thorough explanation of fundamental components such as states, actions, policies, and reward signals, ensuring readers develop a solid foundational understanding. Additionally, the paper presents a variety of RL algorithms, categorized based on the key factors such as model-free, model-based, value-based, policy-based, and other key factors. Resources for learning and implementing RL, such as books, courses, and online communities are also provided. By offering a clear, structured introduction, this paper aims to simplify the complexities of RL for beginners, providing a straightforward pathway to understanding.