Add depreciation

unit1/README.md

@@ -1,4 +1,8 @@
-# Unit 1: Introduction to Deep Reinforcement Learning 🚀
+# DEPRECIATED THE NEW UNIT 1 IS HERE: https://huggingface.co/deep-rl-course/unit1/introduction
+**Everything under is depreciated** 👇, the new version of the course is here: https://huggingface.co/deep-rl-course/unit1/introduction
+
+
+# Unit 1: Introduction to Deep Reinforcement Learning 🚀 (DEPRECIATED)
 
 ![cover](assets/img/thumbnail.png)
 

unit1/unit1.ipynb

@@ -16,6 +16,8 @@
         "id": "njb_ProuHiOe"
       },
       "source": [
+        "# DEPRECIATED NOTEBOOK, THE NEW UNIT 1 IS HERE: https://huggingface.co/deep-rl-course/unit1/introduction
+        **Everything under is depreciated** 👇, the new version of the course is here: https://huggingface.co/deep-rl-course/unit1/introduction"
         "# Unit 1: Train your first Deep Reinforcement Learning Agent 🚀\n",
         "![Cover](https://github.com/huggingface/deep-rl-class/blob/main/unit1/assets/img/thumbnail.png?raw=true)\n",
         "\n",

unit2/README.md

@@ -1,3 +1,7 @@
+"# DEPRECIATED UNIT, THE NEW UNIT 2 IS HERE: https://huggingface.co/deep-rl-course/unit2/introduction
+**Everything under is depreciated** 👇, the new version of the course is here: https://huggingface.co/deep-rl-course/unit2/introduction"
+
+
 # Unit 2: Introduction to Q-Learning
 
 In this Unit, we're going to dive deeper into one of the Reinforcement Learning methods: value-based methods and **study our first RL algorithm: Q-Learning**.
@@ -70,7 +74,7 @@ You can work directly **with the colab notebook, which allows you not to have to
 - To dive deeper on Monte Carlo and Temporal Difference Learning:
   - [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)
- 
+
 ## How to make the most of this course
 
 To make the most of the course, my advice is to:

unit2/unit2.ipynb

@@ -16,6 +16,8 @@
         "id": "njb_ProuHiOe"
       },
       "source": [
+        "# DEPRECIATED NOTEBOOK, THE NEW UNIT 2 IS HERE: https://huggingface.co/deep-rl-course/unit2/introduction
+        **Everything under is depreciated** 👇, the new version of the course is here: https://huggingface.co/deep-rl-course/unit2/introduction"
         "# Unit 2: Q-Learning with FrozenLake-v1 ⛄ and Taxi-v3 🚕\n",
         "\n",
         "In this notebook, **you'll code from scratch your first Reinforcement Learning agent** playing FrozenLake ❄️ using Q-Learning, share it to the community, and experiment with different configurations\n",