update: readme.md

README.md

@@ -58,7 +58,7 @@ The reward function for each action is based on two parts:
 
 <img src="images/standard-deviation.png" />
 
-Where \( \alpha \) is a weighting factor (in this case effectively 1, or any scalar you choose) that determines the penalty's intensity. This keeps the board balanced and helps the agent learn a more efficient Tetris strategy.
+Where alpha is a weighting factor (in this case effectively 1, or any scalar you choose) that determines the penalty's intensity. This keeps the board balanced and helps the agent learn a more efficient Tetris strategy.
 
 ## Installation & Usage
 1. Clone this repo or download the source code.