A Practical Introduction to Deep Reinforcement Learning

TL;DR

This paper provides a practical, beginner-friendly introduction to deep reinforcement learning, focusing on the PPO algorithm within the GPI framework, emphasizing intuition and implementation over complex theory.

Contribution

It offers a unified, accessible tutorial on DRL algorithms, especially PPO, with intuitive explanations and practical insights for newcomers.

Findings

Organizes DRL algorithms under GPI framework for clarity

Focuses on intuitive understanding and practical implementation

Serves as an accessible guide for beginners in DRL

Abstract

Deep reinforcement learning (DRL) has emerged as a powerful framework for solving sequential decision-making problems, achieving remarkable success in a wide range of applications, including game AI, autonomous driving, biomedicine, and large language models. However, the diversity of algorithms and the complexity of theoretical foundations often pose significant challenges for beginners seeking to enter the field. This tutorial aims to provide a concise, intuitive, and practical introduction to DRL, with a particular focus on the Proximal Policy Optimization (PPO) algorithm, which is one of the most widely used and effective DRL methods. To facilitate learning, we organize all algorithms under the Generalized Policy Iteration (GPI) framework, offering readers a unified and systematic perspective. Instead of lengthy theoretical proofs, we emphasize intuitive explanations, illustrative examples, and practical engineering techniques. This work serves as an efficient and accessible guide, helping readers rapidly progress from basic concepts to the implementation of advanced DRL algorithms.