This overview provides a comprehensive and up-to-date summary of deep reinforcement learning (RL). It begins with an introduction to machine learning, deep learning, and reinforcement learning, highlighting their foundational concepts and recent advancements. The core elements of RL, including value functions, policies, rewards, models, planning, exploration, and knowledge, are discussed in detail. Key mechanisms such as attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn are explored. The applications of RL in various fields, such as games (e.g., AlphaGo), robotics, natural language processing (e.g., dialogue systems), computer vision, business management, finance, healthcare, education, Industry 4.0, smart grids, intelligent transportation systems, and computer systems, are surveyed. The overview also includes a list of topics not yet covered and a collection of resources for further study. The authors welcome comments and criticisms and aim to provide a valuable reference for both experts and newcomers to the field.This overview provides a comprehensive and up-to-date summary of deep reinforcement learning (RL). It begins with an introduction to machine learning, deep learning, and reinforcement learning, highlighting their foundational concepts and recent advancements. The core elements of RL, including value functions, policies, rewards, models, planning, exploration, and knowledge, are discussed in detail. Key mechanisms such as attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn are explored. The applications of RL in various fields, such as games (e.g., AlphaGo), robotics, natural language processing (e.g., dialogue systems), computer vision, business management, finance, healthcare, education, Industry 4.0, smart grids, intelligent transportation systems, and computer systems, are surveyed. The overview also includes a list of topics not yet covered and a collection of resources for further study. The authors welcome comments and criticisms and aim to provide a valuable reference for both experts and newcomers to the field.