feat: initiate Korean i18n effort

.github/workflows/build_documentation.yml

@@ -14,7 +14,7 @@ jobs:
       package_name: deep-rl-course
       path_to_docs: deep-rl-class/units/
       additional_args: --not_python_module
-      languages: en
+      languages: en ko
     secrets:
       token: ${{ secrets.HUGGINGFACE_PUSH }}
       hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}

.github/workflows/build_pr_documentation.yml

@@ -17,4 +17,4 @@ jobs:
       package_name: deep-rl-course
       path_to_docs: deep-rl-class/units/
       additional_args: --not_python_module
-      languages: en
+      languages: en ko

units/ko/_toctree.yml

@@ -0,0 +1,232 @@
+- title: (작업중) Unit 0. Welcome to the course
+  sections:
+  - local: ../en/unit0/introduction
+    title: (작업중) Welcome to the course 🤗
+  - local: ../en/unit0/setup
+    title: (작업중) Setup
+  - local: ../en/unit0/discord101
+    title: (작업중) Discord 101
+- title: (작업중) Unit 1. Introduction to Deep Reinforcement Learning
+  sections:
+  - local: ../en/unit1/introduction
+    title: (작업중) Introduction
+  - local: ../en/unit1/what-is-rl
+    title: (작업중) What is Reinforcement Learning?
+  - local: ../en/unit1/rl-framework
+    title: (작업중) The Reinforcement Learning Framework
+  - local: ../en/unit1/tasks
+    title: (작업중) The type of tasks
+  - local: ../en/unit1/exp-exp-tradeoff
+    title: (작업중) The Exploration/ Exploitation tradeoff
+  - local: ../en/unit1/two-methods
+    title: (작업중) The two main approaches for solving RL problems
+  - local: ../en/unit1/deep-rl
+    title: (작업중) The “Deep” in Deep Reinforcement Learning
+  - local: ../en/unit1/summary
+    title: (작업중) Summary
+  - local: ../en/unit1/glossary
+    title: (작업중) Glossary
+  - local: ../en/unit1/hands-on
+    title: (작업중) Hands-on
+  - local: ../en/unit1/quiz
+    title: (작업중) Quiz
+  - local: ../en/unit1/conclusion
+    title: (작업중) Conclusion
+  - local: ../en/unit1/additional-readings
+    title: (작업중) Additional Readings
+- title: (작업중) Bonus Unit 1. Introduction to Deep Reinforcement Learning with Huggy
+  sections:
+  - local: ../en/unitbonus1/introduction
+    title: (작업중) Introduction
+  - local: ../en/unitbonus1/how-huggy-works
+    title: (작업중) How Huggy works?
+  - local: ../en/unitbonus1/train
+    title: (작업중) Train Huggy
+  - local: ../en/unitbonus1/play
+    title: (작업중) Play with Huggy
+  - local: ../en/unitbonus1/conclusion
+    title: (작업중) Conclusion
+- title: (작업중) Live 1. How the course work, Q&A, and playing with Huggy
+  sections:
+  - local: ../en/live1/live1
+    title: (작업중) Live 1. How the course work, Q&A, and playing with Huggy 🐶
+- title: (작업중) Unit 2. Introduction to Q-Learning
+  sections:
+  - local: ../en/unit2/introduction
+    title: (작업중) Introduction
+  - local: ../en/unit2/what-is-rl
+    title: (작업중) What is RL? A short recap
+  - local: ../en/unit2/two-types-value-based-methods
+    title: (작업중) The two types of value-based methods
+  - local: ../en/unit2/bellman-equation
+    title: (작업중) The Bellman Equation, simplify our value estimation
+  - local: ../en/unit2/mc-vs-td
+    title: (작업중) Monte Carlo vs Temporal Difference Learning
+  - local: ../en/unit2/mid-way-recap
+    title: (작업중) Mid-way Recap
+  - local: ../en/unit2/mid-way-quiz
+    title: (작업중) Mid-way Quiz
+  - local: ../en/unit2/q-learning
+    title: (작업중) Introducing Q-Learning
+  - local: ../en/unit2/q-learning-example
+    title: (작업중) A Q-Learning example
+  - local: ../en/unit2/q-learning-recap
+    title: (작업중) Q-Learning Recap
+  - local: ../en/unit2/glossary
+    title: (작업중) Glossary
+  - local: ../en/unit2/hands-on
+    title: (작업중) Hands-on
+  - local: ../en/unit2/quiz2
+    title: (작업중) Q-Learning Quiz
+  - local: ../en/unit2/conclusion
+    title: (작업중) Conclusion
+  - local: ../en/unit2/additional-readings
+    title: (작업중) Additional Readings
+- title: (작업중) Unit 3. Deep Q-Learning with Atari Games
+  sections:
+  - local: ../en/unit3/introduction
+    title: (작업중) Introduction
+  - local: ../en/unit3/from-q-to-dqn
+    title: (작업중) From Q-Learning to Deep Q-Learning
+  - local: ../en/unit3/deep-q-network
+    title: (작업중) The Deep Q-Network (DQN)
+  - local: ../en/unit3/deep-q-algorithm
+    title: (작업중) The Deep Q Algorithm
+  - local: ../en/unit3/glossary
+    title: (작업중) Glossary
+  - local: ../en/unit3/hands-on
+    title: (작업중) Hands-on
+  - local: ../en/unit3/quiz
+    title: (작업중) Quiz
+  - local: ../en/unit3/conclusion
+    title: (작업중) Conclusion
+  - local: ../en/unit3/additional-readings
+    title: (작업중) Additional Readings
+- title: (작업중) Bonus Unit 2. Automatic Hyperparameter Tuning with Optuna
+  sections:
+  - local: ../en/unitbonus2/introduction
+    title: (작업중) Introduction
+  - local: ../en/unitbonus2/optuna
+    title: (작업중) Optuna
+  - local: ../en/unitbonus2/hands-on
+    title: (작업중) Hands-on
+- title: (작업중) Unit 4. Policy Gradient with PyTorch
+  sections:
+  - local: ../en/unit4/introduction
+    title: (작업중) Introduction
+  - local: ../en/unit4/what-are-policy-based-methods
+    title: (작업중) What are the policy-based methods?
+  - local: ../en/unit4/advantages-disadvantages
+    title: (작업중) The advantages and disadvantages of policy-gradient methods
+  - local: ../en/unit4/policy-gradient
+    title: (작업중) Diving deeper into policy-gradient
+  - local: ../en/unit4/pg-theorem
+    title: (작업중) (Optional) the Policy Gradient Theorem
+  - local: ../en/unit4/hands-on
+    title: (작업중) Hands-on
+  - local: ../en/unit4/quiz
+    title: (작업중) Quiz
+  - local: ../en/unit4/conclusion
+    title: (작업중) Conclusion
+  - local: ../en/unit4/additional-readings
+    title: (작업중) Additional Readings
+- title: (작업중) Unit 5. Introduction to Unity ML-Agents
+  sections:
+  - local: ../en/unit5/introduction
+    title: (작업중) Introduction
+  - local: ../en/unit5/how-mlagents-works
+    title: (작업중) How ML-Agents works?
+  - local: ../en/unit5/snowball-target
+    title: (작업중) The SnowballTarget environment
+  - local: ../en/unit5/pyramids
+    title: (작업중) The Pyramids environment
+  - local: ../en/unit5/curiosity
+    title: (작업중) (Optional) What is curiosity in Deep Reinforcement Learning?
+  - local: ../en/unit5/hands-on
+    title: (작업중) Hands-on
+  - local: ../en/unit5/bonus
+    title: (작업중) Bonus. Learn to create your own environments with Unity and MLAgents
+  - local: ../en/unit5/conclusion
+    title: (작업중) Conclusion
+- title: (작업중) Unit 6. Actor Critic methods with Robotics environments
+  sections:
+  - local: ../en/unit6/introduction
+    title: (작업중) Introduction
+  - local: ../en/unit6/variance-problem
+    title: (작업중) The Problem of Variance in Reinforce
+  - local: ../en/unit6/advantage-actor-critic
+    title: (작업중) Advantage Actor Critic (A2C)
+  - local: ../en/unit6/hands-on
+    title: (작업중) Advantage Actor Critic (A2C) using Robotics Simulations with PyBullet and Panda-Gym 🤖
+  - local: ../en/unit6/conclusion
+    title: (작업중) Conclusion
+  - local: ../en/unit6/additional-readings
+    title: (작업중) Additional Readings
+- title: (작업중) Unit 7. Introduction to Multi-Agents and AI vs AI
+  sections:
+  - local: ../en/unit7/introduction
+    title: (작업중) Introduction
+  - local: ../en/unit7/introduction-to-marl
+    title: (작업중) An introduction to Multi-Agents Reinforcement Learning (MARL)
+  - local: ../en/unit7/multi-agent-setting
+    title: (작업중) Designing Multi-Agents systems
+  - local: ../en/unit7/self-play
+    title: (작업중) Self-Play
+  - local: ../en/unit7/hands-on
+    title: (작업중) Let's train our soccer team to beat your classmates' teams (AI vs. AI)
+  - local: ../en/unit7/conclusion
+    title: (작업중) Conclusion
+  - local: ../en/unit7/additional-readings
+    title: (작업중) Additional Readings
+- title: (작업중) Unit 8. Part 1 Proximal Policy Optimization (PPO)
+  sections:
+  - local: ../en/unit8/introduction
+    title: (작업중) Introduction
+  - local: ../en/unit8/intuition-behind-ppo
+    title: (작업중) The intuition behind PPO
+  - local: ../en/unit8/clipped-surrogate-objective
+    title: (작업중) Introducing the Clipped Surrogate Objective Function
+  - local: ../en/unit8/visualize
+    title: (작업중) Visualize the Clipped Surrogate Objective Function
+  - local: ../en/unit8/hands-on-cleanrl
+    title: (작업중) PPO with CleanRL
+  - local: ../en/unit8/conclusion
+    title: (작업중) Conclusion
+  - local: ../en/unit8/additional-readings
+    title: (작업중) Additional Readings
+- title: (작업중) Unit 8. Part 2 Proximal Policy Optimization (PPO) with Doom
+  sections:
+  - local: ../en/unit8/introduction-sf
+    title: (작업중) Introduction
+  - local: ../en/unit8/hands-on-sf
+    title: (작업중) PPO with Sample Factory and Doom
+  - local: ../en/unit8/conclusion-sf
+    title: (작업중) Conclusion
+- title: (작업중) Bonus Unit 3. Advanced Topics in Reinforcement Learning
+  sections:
+  - local: ../en/unitbonus3/introduction
+    title: (작업중) Introduction
+  - local: ../en/unitbonus3/model-based
+    title: (작업중) Model-Based Reinforcement Learning
+  - local: ../en/unitbonus3/offline-online
+    title: (작업중) Offline vs. Online Reinforcement Learning
+  - local: ../en/unitbonus3/rlhf
+    title: (작업중) Reinforcement Learning from Human Feedback
+  - local: ../en/unitbonus3/decision-transformers
+    title: (작업중) Decision Transformers and Offline RL
+  - local: ../en/unitbonus3/language-models
+    title: (작업중) Language models in RL
+  - local: ../en/unitbonus3/curriculum-learning
+    title: (작업중) (Automatic) Curriculum Learning for RL
+  - local: ../en/unitbonus3/envs-to-try
+    title: (작업중) Interesting environments to try
+  - local: ../en/unitbonus3/godotrl
+    title: (작업중) An Introduction to Godot RL
+  - local: ../en/unitbonus3/rl-documentation
+    title: (작업중) Brief introduction to RL documentation
+- title: (작업중) Certification and congratulations
+  sections:
+  - local: ../en/communication/conclusion
+    title: (작업중) Congratulations
+  - local: ../en/communication/certification
+    title: (작업중) Get your certificate of completion