How can I improve sample efficiency in my machine learning model?

How can I improve sample efficiency in my machine learning model?

Improving sample efficiency often involves techniques like transfer learning, data augmentation, and using more informative priors. What specific area are you focusing on?

I'm working on reinforcement learning. Any specific strategies?

In reinforcement learning, you can use techniques like experience replay, prioritized experience replay, and reward shaping to enhance sample efficiency.

Can you explain experience replay?

Experience replay involves storing past experiences and reusing them during training. This helps the model learn from a diverse set of experiences, improving efficiency.

What about data augmentation? How does it help?

Data augmentation artificially increases the size of your training dataset by creating modified versions of existing data. This helps the model generalize better and reduces overfitting.