diff --git a/units/en/unit3/quiz.mdx b/units/en/unit3/quiz.mdx
index 841ee4d..13f0295 100644
--- a/units/en/unit3/quiz.mdx
+++ b/units/en/unit3/quiz.mdx
@@ -76,8 +76,8 @@ For instance, in pong, our agent **will be unable to know the ball direction if
 
 **1. Make more efficient use of the experiences during the training**
 
-Usually, in online reinforcement learning, the agent interacts in the environment, gets experiences (state, action, reward, and next state), learns from them (updates the neural network), and discards them. This is not efficient
-But with experience replay, **we create a replay buffer that saves experience samples that we can reuse during the training**.
+Usually, in online reinforcement learning, the agent interacts in the environment, gets experiences (state, action, reward, and next state), learns from them (updates the neural network), and discards them. This is not efficient.
+But, with experience replay, **we create a replay buffer that saves experience samples that we can reuse during the training**.
 
 **2. Avoid forgetting previous experiences and reduce the correlation between experiences**
 
diff --git a/units/en/unit4/what-are-policy-based-methods.mdx b/units/en/unit4/what-are-policy-based-methods.mdx
index b06ddac..0330e29 100644
--- a/units/en/unit4/what-are-policy-based-methods.mdx
+++ b/units/en/unit4/what-are-policy-based-methods.mdx
@@ -37,6 +37,6 @@ Policy-gradient methods, what we're going to study in this unit, is a subclass o
 The difference between these two methods **lies on how we optimize the parameter** \\(\theta\\):
 
 - In *policy-based methods*, we search directly for the optimal policy. We can optimize the parameter \\(\theta\\) **indirectly** by maximizing the local approximation of the objective function with techniques like hill climbing, simulated annealing, or evolution strategies.
-- In *policy-gradient methods*, because we're a subclass of the policy-based methods, we search directly for the optimal policy. But we optimize the parameter \\(\theta\\) **directly** by performing the gradient ascent on the performance of the objective function \\(J(\theta)\\).
+- In *policy-gradient methods*, because it is a subclass of the policy-based methods, we search directly for the optimal policy. But we optimize the parameter \\(\theta\\) **directly** by performing the gradient ascent on the performance of the objective function \\(J(\theta)\\).
 
-Before diving more into how works policy-gradient methods (the objective function, policy gradient theorem, gradient ascent, etc.), let's study the advantages and disadvantages of policy-based methods.
+Before diving more into how policy-gradient methods work (the objective function, policy gradient theorem, gradient ascent, etc.), let's study the advantages and disadvantages of policy-based methods.