diff --git a/units/en/unit1/rl-framework.mdx b/units/en/unit1/rl-framework.mdx
index fbba374..1af2291 100644
--- a/units/en/unit1/rl-framework.mdx
+++ b/units/en/unit1/rl-framework.mdx
@@ -61,8 +61,6 @@ In a chess game, we have access to the whole board information, so we receive a
 <figcaption>In Super Mario Bros, we only see the part of the level close to the player, so we receive an observation.</figcaption>
 </figure>
 
-In Super Mario Bros, we only see the part of the level close to the player, so we receive an observation.
-
 In Super Mario Bros, we are in a partially observed environment. We receive an observation **since we only see a part of the level.**
 
 <Tip>
@@ -87,8 +85,6 @@ The actions can come from a *discrete* or *continuous space*:
 
 </figure>
 
-Again, in Super Mario Bros, we have a finite set of actions since we have only 4 directions.
-
 - *Continuous space*: the number of possible actions is **infinite**.
 
 <figure>
diff --git a/units/en/unit1/two-methods.mdx b/units/en/unit1/two-methods.mdx
index fcfc04a..34ddab8 100644
--- a/units/en/unit1/two-methods.mdx
+++ b/units/en/unit1/two-methods.mdx
@@ -82,8 +82,6 @@ Here we see that our value function **defined values for each possible state.**
 <figcaption>Thanks to our value function, at each step our policy will select the state with the biggest value defined by the value function: -7, then -6, then -5 (and so on) to attain the goal.</figcaption>
 </figure>
 
-Thanks to our value function, at each step our policy will select the state with the biggest value defined by the value function: -7, then -6, then -5 (and so on) to attain the goal.
-
 If we recap:
 
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/vbm_1.jpg" alt="Vbm recap" width="100%" />