Updated the Read Me

README.md

@@ -70,20 +70,20 @@ To evaluate the trained model, execute the following:
 python eval.py
 ```
 
-After running this command, the root mean squared error (RMSE), mean R-squared and Total Rewards will be plotted.
+After running this command, the root mean squared error (RMSE), the MAPE and Total Rewards will be plotted.
 
 ---
 
 ## 5. Fine Tuning the LSTM RL Model
 
-Fine tuning the LSTM RL model will take more time as it will train until it reaches a specific R2 threshold. By default, the R2 threshold is set to 2.982 (An R2 average of 0.994). However, if you want to set a different R2 threshold, open the fine_tune.py file and change the accuracy_threshold variable to your desired target accuracy.
+Fine tuning the LSTM RL model will take more time as it will train until it reaches a specific reward threshold. By default, the reward threshold is set to 0.99 (An MAPE average of 0.01). However, if you want to set a different reward threshold, open the fine_tune.py file and change the reward_threshold variable to your desired target accuracy.
 
 To fine tune the model, execute the following:
 ```
 python fine_tune.py
 ```
 
-It's advisable to stay hydrated with some water while waiting for the fine-tuning process to finish, as it may take some time. Once the fine-tuning is complete, attempt to run the "finetune.py" script until it loops only three times. If it doesnt loop 3 times, keep rerunning it until it has looped three times at least once.
+It's advisable to stay hydrated with some water while waiting for the fine-tuning process to finish, as it may take some time. Once the fine-tuning is complete, attempt to run the "fine_tune.py" script until it loops only three times. If it doesnt loop 3 times, keep rerunning it until it has looped three times at least once.
 
 After fine-tuning is complete, it is recommended to re-evaluate the model.