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Update readme for part 2

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@@ -56,6 +56,22 @@ Are you new to Discord? Check our **discord 101 to get the best practices** 👉
- [Why do temporal difference (TD) methods have lower variance than Monte Carlo methods?](https://stats.stackexchange.com/questions/355820/why-do-temporal-difference-td-methods-have-lower-variance-than-monte-carlo-met)
- [When are Monte Carlo methods preferred over temporal difference ones?](https://stats.stackexchange.com/questions/336974/when-are-monte-carlo-methods-preferred-over-temporal-difference-ones)
4️⃣ 📖 **Read An [Introduction to Q-Learning Part 2](https://huggingface.co/blog/deep-rl-q-part2)**.
5️⃣ 👩‍💻 Then dive on the hands-on, where **you’ll implement our first RL agent from scratch**, a Q-Learning agent, and will train it in two environments:
1. Frozen Lake v1 ❄️: where our agent will need to **go from the starting state (S) to the goal state (G)** by walking only on frozen tiles (F) and avoiding holes (H).
2. An autonomous taxi 🚕: where the agent will need **to learn to navigate** a city to **transport its passengers from point A to point B.**
Thanks to a leaderboard, **you'll be able to compare your results with other classmates** and exchange the best practices to improve your agent's scores Who will win the challenge for Unit 2 🏆?
The hands-on 👉 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/main/unit2/unit2.ipynb)
The leaderboard 👉 https://huggingface.co/spaces/chrisjay/Deep-Reinforcement-Learning-Leaderboard
You can work directly **with the colab notebook, which allows you not to have to install everything on your machine (and it’s free)**.
6️⃣ The best way to learn **is to try things on your own**. That’s why we have a challenges section in the colab where we give you some ideas on how you can go further: using another environment, using another model etc.
## How to make the most of this course
To make the most of the course, my advice is to: