Reinforcement Learning and Deep RL Python Theory and Projects - Quiz (Number of Episodes)

The video tutorial discusses the concept of hyperparameters, focusing on the number of episodes in a machine learning context. It questions the adequacy of setting the maximum number of episodes to 10,000 and explores how to determine the right number. The tutorial encourages reflection on whether the chosen number is sufficient and how to evaluate its effectiveness.

What is a hyperparameter discussed in the video?

• Number of episodes

• Learning rate

• Batch size

• Activation function

In the example given, what is the maximum number of episodes set in the code?

• 100,000

• 10,000

• 5,000

• 1,000

Why might the number of episodes need to be adjusted?

• To reduce computation time

• All of the above

• To improve model accuracy

• To ensure model convergence

What is a potential issue with setting the number of episodes too high?

• Decreased model accuracy

• Increased computation cost

• Underfitting

• Overfitting

How can one determine if the number of episodes is appropriate?

• By checking the model's performance

• By reducing the learning rate

• By increasing the batch size

• By changing the activation function