A Practical Introduction to Deep Reinforcement Learning
On the map: Deep reinforcement learning methods → Deep RL algorithms and representations
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 14 references that are in the map. First posted on arXiv 13 May 2025.
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5 of 14 references.
References · 5
- 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
- 2022Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learningcited by 28
- 2024Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space Pose Tracking with a Wheeled-Quadrupedal-Manipulatorcited by 10
- 2024Efficient and Generalized end-to-end Autonomous Driving System with Latent Deep Reinforcement Learning and Demonstrationscited by 7
- 2024UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecutioncited by 5
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- 2024A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges
- 2024Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm
- 2023Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization
- 2024Introduction to Reinforcement Learning
- 2024The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations
- 2026Deep Reinforcement Learning: From First Principles to Reasoning Models ↗
- 2017Proximal Policy Optimization Algorithms
- 2021On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning