A Practical Introduction to Deep Reinforcement Learning
It addresses the problem for newcomers to DRL by offering an accessible guide, but it is incremental as it repackages existing methods for educational purposes.
This tutorial tackles the challenge of beginners struggling with the complexity and diversity of deep reinforcement learning algorithms by providing a concise, practical introduction focused on Proximal Policy Optimization (PPO) and organized under the Generalized Policy Iteration framework.
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.