Update Course

notebooks/unit3/unit3.ipynb

@@ -289,6 +289,7 @@
       },
       "outputs": [],
       "source": [
+        "pip install setuptools==65.5.0\n",
         "!pip install -r requirements.txt\n",
         "# Since colab uses Python 3.9 we need to add this installation\n",
         "!pip install gym[atari,accept-rom-license]==0.21.0"

units/en/unit3/hands-on.mdx

@@ -127,7 +127,10 @@ cd /content/rl-baselines3-zoo/
 ```
 
 ```bash
+pip install setuptools==65.5.0
 pip install -r requirements.txt
+# Since colab uses Python 3.9 we need to add this installation
+pip install gym[atari,accept-rom-license]==0.21.0
 ```
 
 ## Train our Deep Q-Learning Agent to Play Space Invaders 👾

units/en/unit7/self-play.mdx

@@ -31,7 +31,7 @@ We do the same with self-play:
 - We **start with a copy of our agent as an opponent** this way, this opponent is on a similar level.
 - We **learn from it**, and when we acquire some skills, we **update our opponent with a more recent copy of our training policy**.
 
-The theory behind self-play is not something new. It was already used by Arthur Samuel’s checker player system in the fifties and by Gerald Tesauro’s TD-Gammon in 1955. If you want to learn more about the history of self-play [check this very good blogpost by Andrew Cohen](https://blog.unity.com/technology/training-intelligent-adversaries-using-self-play-with-ml-agents)
+The theory behind self-play is not something new. It was already used by Arthur Samuel’s checker player system in the fifties and by Gerald Tesauro’s TD-Gammon in 1995. If you want to learn more about the history of self-play [check this very good blogpost by Andrew Cohen](https://blog.unity.com/technology/training-intelligent-adversaries-using-self-play-with-ml-agents)
 
 ## Self-Play in MLAgents