diff --git a/units/en/unit2/bellman-equation.mdx b/units/en/unit2/bellman-equation.mdx
index be70441..27c7d35 100644
--- a/units/en/unit2/bellman-equation.mdx
+++ b/units/en/unit2/bellman-equation.mdx
@@ -42,7 +42,7 @@ If we go back to our example, the value of State 1= expected cumulative return i
 
 To calculate the value of State 1: the sum of rewards **if the agent started in that state 1** and then followed the **policy for all the time steps.**
 
-Which is equivalent to  \\(V(S_{t})\\)  = Immediate reward  \\(R_{t+1}\\)  + Discounted value of the next state  \\(gamma * V(S_{t+1})\\)
+This is equivalent to  \\(V(S_{t})\\)  = Immediate reward  \\(R_{t+1}\\)  + Discounted value of the next state  \\(gamma * V(S_{t+1})\\)
 
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/bellman6.jpg" alt="Bellman equation"/>