Translating to Russian

Hi!

Let's bring the reinforcement learning course to all the Russian-speaking community 🌏

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 `ru` inside the [source folder](https://github.com/huggingface/deep-rl-class/tree/main/units).
Register your translation in `ru/_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 @blademoon for review.

- [X] Unit 0. Welcome to the course @blademoon
- [X] Welcome to the course 🤗 @blademoon
- [X] Setup @blademoon
- [X] Discord 101 @blademoon
- [X] Unit 1. Introduction to Deep Reinforcement Learning @blademoon
- [X] Introduction @blademoon
- [X] What is Reinforcement Learning? @blademoon
- [X] The Reinforcement Learning Framework @blademoon
- [X] The type of tasks @blademoon
- [X] The Exploration/ Exploitation tradeoff @blademoon
- [X] The two main approaches for solving RL problems @blademoon
- [X] The “Deep” in Deep Reinforcement Learning @blademoon
- [X] Summary @blademoon
- [X] Glossary @blademoon
- [X] Hands-on @blademoon
- [X] Quiz @blademoon
- [X] Conclusion @blademoon
- [X] Additional Readings @blademoon
- [X] Bonus Unit 1. Introduction to Deep Reinforcement Learning with Huggy @blademoon
- [X] Introduction @blademoon
- [X] How Huggy works? @blademoon
- [X] Train Huggy @blademoon
- [X] Play with Huggy @blademoon
- [X] Conclusion @blademoon
- [X] Live 1. How the course work, Q&A, and playing with Huggy @blademoon
- [X] Live 1. How the course work, Q&A, and playing with Huggy 🐶 @blademoon
- [X] Unit 2. Introduction to Q-Learning @blademoon
- [X] Introduction @blademoon
- [X] What is RL? A short recap @blademoon
- [X] The two types of value-based methods @blademoon
- [X] The Bellman Equation, simplify our value estimation @blademoon
- [X] Monte Carlo vs Temporal Difference Learning @blademoon
- [X] Mid-way Recap @blademoon
- [X] Mid-way Quiz @blademoon
- [X] Introducing Q-Learning @blademoon
- [X] A Q-Learning example @blademoon
- [X] Q-Learning Recap @blademoon
- [X] Glossary @blademoon
- [X] Hands-on @blademoon
- [X] Q-Learning Quiz @blademoon
- [X] Conclusion @blademoon
- [X] Additional Readings @blademoon
- [ ] Unit 3. Deep Q-Learning with Atari Games @blademoon
- [X] Introduction @blademoon
- [X] From Q-Learning to Deep Q-Learning @blademoon
- [X] The Deep Q-Network (DQN) @blademoon
- [X] The Deep Q Algorithm @blademoon
- [X] Glossary @blademoon
- [ ] Hands-on @blademoon 🎯
- [X] Quiz @blademoon
- [X] Conclusion @blademoon
- [X] Additional Readings @blademoon
- [X] Bonus Unit 2. Automatic Hyperparameter Tuning with Optuna @blademoon
- [X] Introduction @blademoon
- [X] Optuna @blademoon
- [X] Hands-on @blademoon
- [ ] Unit 4. Policy Gradient with PyTorch @kozhukovv
- [ ] Introduction @kozhukovv
- [ ] What are the policy-based methods? @kozhukovv
- [ ] The advantages and disadvantages of policy-gradient methods @kozhukovv
- [ ] Diving deeper into policy-gradient @kozhukovv
- [ ] (Optional) the Policy Gradient Theorem @kozhukovv
- [ ] Hands-on @kozhukovv
- [ ] Quiz @kozhukovv
- [ ] Conclusion @kozhukovv
- [ ] Additional Readings @kozhukovv
- [ ] 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) @kozhukovv
- [ ] Introduction @kozhukovv
- [ ] The intuition behind PPO @kozhukovv
- [ ] Introducing the Clipped Surrogate Objective Function @kozhukovv
- [ ] Visualize the Clipped Surrogate Objective Function @kozhukovv
- [ ] PPO with CleanRL @kozhukovv
- [ ] Conclusion @kozhukovv
- [ ] Additional Readings @kozhukovv
- [ ] Unit 8. Part 2 Proximal Policy Optimization (PPO) with Doom @kozhukovv
- [ ] Introduction @kozhukovv
- [ ] PPO with Sample Factory and Doom @kozhukovv
- [ ] Conclusion @kozhukovv
- [ ] 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

Notebooks:
- [X] Unit 1 Notebook @abbbe
- [ ] Bonus Unit 1 Notebook @abbbe
- [X] Unit 2 Notebook @blademoon
- [X] Unit 3 Notebook @blademoon
- [X] Bonus Unit 3 icra22_optuna_lab.ipynb @blademoon
- [ ] Unit 4 Notebook 🎯
- [ ] Unit 5 Notebook
- [ ] Unit 6 Notebook

Video subtitles:
- [ ] Bonus Unit 3, Automatic Hyperparameter Optimization @ ICRA 22 | Tools for Robotic RL 6/8
- [ ] Bonus Unit 3, Hyperparameter Tuning with Optuna Notebook @ ICRA 22 | Tools for Robotic RL 7/8