Reinforcement Learning

• What is the title of the book authored by Dong-Chul Kim?

  CSCI6370 Intro to Reinforcement Learning: Theory and Practice
• What is the primary purpose of the book CSCI6370 Intro to Reinforcement Learning?

  To help students study the material before class and review it afterward
• Who is the book dedicated to?

  Chick-fil-A
• What is the first edition publication year of the book?

  2024
• What license is the work published under?

  Creative Commons “Attribution-NonCommercial-ShareAlike 3.0 Unported” license
• What is the ISBN of the book?


  000-0000000000
• What prerequisites are required for the CSCI6370 course?

  Intermediate proficiency in Python, university-level calculus, statistics, probability, and linear algebra
• What is required for students to write and format research papers and assignments?

  Familiarity with LaTeX
• What deep learning framework should students have experience using?

  PyTorch
• What type of computer is essential for the course?

  A personal computer with a GPU
• What GPU is required for the Nvidia Isaac Sim section?

  RTX 3070 or higher
• What is the minimum system memory required for the Nvidia Isaac Sim section?

  32GB or more
• What are the four parts of the book structure?

  • Part 1: Fundamentals of machine learning and deep neural networks
  • Part 2: Traditional RL methods and basics of DRL
  • Part 3: Simulation environments for reinforcement learning
  • Part 4: Advanced RL methods

  
• What is covered in Part 1 of the book?

  The fundamentals of machine learning and deep neural networks
• How many chapters are included in Part 2 of the book?

  Two chapters
• What practical applications are explored in Part 3 of the book?

  Use of simulation environments for reinforcement learning
• What does Part 4 of the book address?

  Advanced reinforcement learning methods
• Why is revisiting deep learning concepts important for mastering DRL?

  They are crucial for understanding more advanced reinforcement learning subjects
• What might affect the coverage of chapters in Part 4 during class?

  Time constraints
• What is the significance of the deep learning concepts discussed in Part 1?

  They provide a solid understanding necessary for DRL
• What is the chapter structure of Part 1?

  Chapters 1 to 5 cover various aspects of machine learning and neural networks
• What is the focus of Chapter 1?

  Overview of Machine Learning
• What is the focus of Chapter 2?

  Regression and Classification
• What is the focus of Chapter 3?

  Neural Networks
• What is the focus of Chapter 4?

  Deep Neural Networks
• What is the focus of Chapter 5?

  Convolutional Neural Networks
• What is the focus of Chapter 11?

  Fundamentals of Reinforcement Learning
• What is the focus of Chapter 12?

  Deep Reinforcement Learning
• What is the focus of Chapter 13?

  Pygame and OpenAI Gym
• What is the focus of Chapter 14?

  Mujoco
• What is the focus of Chapter 15?

  Unity ML-Agent
• What is the focus of Chapter 16?

  Isaac Sim
• What is the focus of Chapter 17?

  Offline Reinforcement Learning
• What is the focus of Chapter 18?

  Hierarchical Reinforcement Learning
• What is the focus of Chapter 19?

  Multi-agent Reinforcement Learning
• What is the focus of Chapter 20?

  Transformer Reinforcement Learning
• What is the focus of Chapter 21?

  Graph-Based Reinforcement Learning
• What is the focus of Chapter 22?

  Inverse Reinforcement Learning
• What are the basic concepts of machine learning discussed in the book?

  Relationship between data, models, and learning algorithms; training set, validation set, and test set; overfitting and underfitting; performance evaluation metrics

  
• What challenges in machine learning are addressed in the book?

  Data quality and quantity, computational complexity, model interpretability, ethical considerations, continuous learning