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

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](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md). 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](https://github.com/huggingface/deep-rl-class/tree/main/units).
* Register your translation in `ko/_toctree.yml`; please follow the order of the [English version](https://github.com/huggingface/deep-rl-class/blob/main/units/en/_toctree.yml).
* 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 가이드](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md)를 먼저 읽어보시기 바랍니다. 끝 부분에 번역해야할 파일들이 나열되어 있습니다. 작업하고 계신 파일이 있다면 여기에 간단히 알려주세요. 중복되지 않도록 `작업중`으로 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](https://github.com/huggingface/deep-rl-class/tree/main/units) 아래 `ko` 폴더에 번역본을 넣어주세요.
* 목차(`ko/_toctree.yml`)도 함께 업데이트해주세요. [영어 목차](https://github.com/huggingface/deep-rl-class/blob/main/docs/units/en/_toctree.yml)와 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! 🤗 [포럼](https://discuss.huggingface.co/)에 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