This overview provides a comprehensive introduction to deep reinforcement learning (deep RL), covering six core elements, six important mechanisms, and twelve applications. It begins with background information on machine learning, deep learning, and reinforcement learning. The core elements of RL include value functions, policies, rewards, models, planning, exploration, and knowledge. Important mechanisms include attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn. Applications of RL span various domains, including games (e.g., AlphaGo), robotics, natural language processing, computer vision, business management, finance, healthcare, education, Industry 4.0, smart grids, intelligent transportation systems, and computer systems. The overview also discusses topics not yet covered and lists resources for further study. It concludes with a brief summary and discussions. The text highlights the integration of deep learning with RL, which has led to significant advancements in various fields. It emphasizes the role of deep learning in enabling automatic feature engineering, end-to-end learning, and the ability to handle complex tasks. The overview also discusses challenges in RL, such as the deadly triad issue, and the importance of function approximation, bootstrapping, and off-policy learning. The text concludes by noting the potential of deep RL in achieving artificial general intelligence.This overview provides a comprehensive introduction to deep reinforcement learning (deep RL), covering six core elements, six important mechanisms, and twelve applications. It begins with background information on machine learning, deep learning, and reinforcement learning. The core elements of RL include value functions, policies, rewards, models, planning, exploration, and knowledge. Important mechanisms include attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn. Applications of RL span various domains, including games (e.g., AlphaGo), robotics, natural language processing, computer vision, business management, finance, healthcare, education, Industry 4.0, smart grids, intelligent transportation systems, and computer systems. The overview also discusses topics not yet covered and lists resources for further study. It concludes with a brief summary and discussions. The text highlights the integration of deep learning with RL, which has led to significant advancements in various fields. It emphasizes the role of deep learning in enabling automatic feature engineering, end-to-end learning, and the ability to handle complex tasks. The overview also discusses challenges in RL, such as the deadly triad issue, and the importance of function approximation, bootstrapping, and off-policy learning. The text concludes by noting the potential of deep RL in achieving artificial general intelligence.