What are some common mistakes in reinforcement learning?

What are some common mistakes in reinforcement learning?

Some common mistakes include not properly tuning hyperparameters, neglecting exploration, and overfitting the model.

Can you explain why hyperparameter tuning is important?

Hyperparameter tuning is crucial because it directly affects the learning process and performance of the model.

What do you mean by neglecting exploration?

Neglecting exploration means the agent may not try enough new actions, leading to suboptimal learning and performance.

How can I avoid overfitting in my model?

You can avoid overfitting by using techniques like regularization, early stopping, and ensuring a diverse training dataset.