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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>

units/en/unit1/rl-framework.mdx

@@ -126,7 +126,7 @@ Which is equivalent to:
 
 However, in reality, **we can’t just add them like that.** The rewards that come sooner (at the beginning of the game) **are more likely to happen** since they are more predictable than the long-term future reward.
 
-Let’s say your agent is this tiny mouse that can move one tile each time step, and your opponent is the cat (that can move too). Your goal is **to eat the maximum amount of cheese before being eaten by the cat.**
+Let’s say your agent is this tiny mouse that can move one tile each time step, and your opponent is the cat (that can move too). The mouse's goal is **to eat the maximum amount of cheese before being eaten by the cat.**
 
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/rewards_3.jpg" alt="Rewards" width="100%">
 
@@ -142,5 +142,5 @@ To discount the rewards, we proceed like this:
 
 2. Then, each reward will be discounted by gamma to the exponent of the time step. As the time step increases, the cat gets closer to us, **so the future reward is less and less likely to happen.**
 
-Our discounted cumulative expected rewards is:
+Our discounted expected cumulative reward is:
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/rewards_4.jpg" alt="Rewards" width="100%">

units/en/unit1/summary.mdx

@@ -1,6 +1,6 @@
 # Summary [[summary]]
 
-That was a lot of information, if we summarize:
+That was a lot of information! Let's summarize:
 
 - Reinforcement Learning is a computational approach of learning from action. We build an agent that learns from the environment **by interacting with it through trial and error** and receiving rewards (negative or positive) as feedback.