🌐 [i18n-KO] Translating rl-course to Korean

[wonhyeongseo]

Hi!

Let's bring the reinforcement learning course to all the Korean-speaking community 🌏 (currently 9 out of 77 complete)

Would you want to translate? Please follow the 🤗 TRANSLATING guide. Here is a list of the files ready for translation. Let us know in this issue if you'd like to translate any, and we'll add your name to the list.

Some notes:

• Please translate using an informal tone (imagine you are talking with a friend about transformers 🤗).
• Please translate in a gender-neutral way.
• Add your translations to the folder called ko inside the source folder.
• Register your translation in ko/_toctree.yml; please follow the order of the English version.
• Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @simoninithomas for review.

안녕하세요!

한국어를 사용하는 모두가 강화학습 코스를 읽을 수 있게 해보아요 🌏

번역에 참여하고 싶으신가요? 🤗 번역 가이드를 먼저 읽어보시기 바랍니다. 끝 부분에 번역해야할 파일들이 나열되어 있습니다. 작업하고 계신 파일이 있다면 여기에 간단히 알려주세요. 중복되지 않도록 작업중으로 표시해둘게요.

참고 사항:

• 기술 문서이지만 (친구에게 설명 듣듯이) 쉽게 읽히면 좋겠습니다. 존댓말 로 써주시면 감사하겠습니다.
• 성별은 일부 언어(스페인어, 프랑스어 등)에만 적용되는 사항으로, 한국어의 경우 번역기를 사용하신 후 문장 기호와 조사 등이 알맞는지 확인해주시기 바랍니다.
• 소스 폴더 아래 ko 폴더에 번역본을 넣어주세요.
• 목차(ko/_toctree.yml)도 함께 업데이트해주세요. 영어 목차와 순서가 동일해야 합니다.
• 모두 마치셨다면, 기록이 원활하도록 PR을 여실 때 현재 이슈(``)를 내용에 넣어주시기 바랍니다. 리뷰 요청은 @simoninithomas 님께 요청해주세요.
• 🙋 커뮤니티에 마음껏 홍보해주시기 바랍니다! 🤗 포럼에 올리셔도 좋아요.

• Unit 0. Welcome to the course
  • Welcome to the course 🤗
  • Setup
  • Discord 101
• Unit 1. Introduction to Deep Reinforcement Learning
  • Introduction
  • What is Reinforcement Learning?
  • The Reinforcement Learning Framework
  • The type of tasks
  • The Exploration/ Exploitation tradeoff
  • The two main approaches for solving RL problems
  • The “Deep” in Deep Reinforcement Learning
  • Summary
  • Glossary
  • Hands-on
  • Quiz
  • Conclusion
  • Additional Readings
• Bonus Unit 1. Introduction to Deep Reinforcement Learning with Huggy
  • Introduction
  • How Huggy works?
  • Train Huggy
  • Play with Huggy
  • Conclusion
• Live 1. How the course work, Q&A, and playing with Huggy
  • Live 1. How the course work, Q&A, and playing with Huggy 🐶
• Unit 2. Introduction to Q-Learning
  • Introduction
  • What is RL? A short recap
  • The two types of value-based methods
  • The Bellman Equation, simplify our value estimation
  • Monte Carlo vs Temporal Difference Learning
  • Mid-way Recap
  • Mid-way Quiz
  • Introducing Q-Learning
  • A Q-Learning example
  • Q-Learning Recap
  • Glossary
  • Hands-on
  • Q-Learning Quiz
  • Conclusion
  • Additional Readings
• Unit 3. Deep Q-Learning with Atari Games
  • Introduction
  • From Q-Learning to Deep Q-Learning
  • The Deep Q-Network (DQN)
  • The Deep Q Algorithm
  • Glossary
  • Hands-on
  • Quiz
  • Conclusion
  • Additional Readings
• Bonus Unit 2. Automatic Hyperparameter Tuning with Optuna
  • Introduction
  • Optuna
  • Hands-on
• Unit 4. Policy Gradient with PyTorch
  • Introduction
  • What are the policy-based methods?
  • The advantages and disadvantages of policy-gradient methods
  • Diving deeper into policy-gradient
  • (Optional) the Policy Gradient Theorem
  • Hands-on
  • Quiz
  • Conclusion
  • Additional Readings
• Unit 5. Introduction to Unity ML-Agents
  • Introduction
  • How ML-Agents works?
  • The SnowballTarget environment
  • The Pyramids environment
  • (Optional) What is curiosity in Deep Reinforcement Learning?
  • Hands-on
  • Bonus. Learn to create your own environments with Unity and MLAgents
  • Conclusion
• Unit 6. Actor Critic methods with Robotics environments
  • Introduction
  • The Problem of Variance in Reinforce
  • Advantage Actor Critic (A2C)
  • Hands-on: Advantage Actor Critic (A2C) using Robotics Simulations with PyBullet and Panda-Gym 🤖
  • Conclusion
  • Additional Readings
• Unit 7. Introduction to Multi-Agents and AI vs AI
  • Introduction
  • An introduction to Multi-Agents Reinforcement Learning (MARL)
  • Designing Multi-Agents systems
  • Self-Play
  • Hands-on: Let's train our soccer team to beat your classmates' teams (AI vs. AI)
  • Conclusion
  • Additional Readings
• Unit 8. Part 1 Proximal Policy Optimization (PPO)
  • Introduction
  • The intuition behind PPO
  • Introducing the Clipped Surrogate Objective Function
  • Visualize the Clipped Surrogate Objective Function
  • PPO with CleanRL
  • Conclusion
  • Additional Readings
• Unit 8. Part 2 Proximal Policy Optimization (PPO) with Doom
  • Introduction
  • PPO with Sample Factory and Doom
  • Conclusion
• Bonus Unit 3. Advanced Topics in Reinforcement Learning
  • Introduction
  • Model-Based Reinforcement Learning
  • Offline vs. Online Reinforcement Learning
  • Reinforcement Learning from Human Feedback
  • Decision Transformers

[simoninithomas]

Hey there👋 thanks for your work. As mentionned in Discord we don't support other languages for this course for now contrary to the transformer course.

However, what we can do is I can create a moon-ci-docs.huggingface.co link that will allow you to share to people who want to follow the course in Korean (and also see what the course looks like).

Currently there's an error in the Build PR documentation so I can't provide you this link (check the Failing error, from what I see is because you don't have a table of contents

Have a nice day 🤗