What is sample efficiency in reinforcement learning?

What is sample efficiency in reinforcement learning?

Sample efficiency refers to how effectively a reinforcement learning algorithm uses the data it collects to learn.

Why is sample efficiency important?

It's crucial because collecting data can be expensive or time-consuming, so efficient algorithms learn faster with less data.

Can you give an example of a sample-efficient algorithm?

Sure! Algorithms like Proximal Policy Optimization (PPO) and Trust Region Policy Optimization (TRPO) are known for their sample efficiency.

How can I improve sample efficiency in my RL model?

You can improve it by using techniques like experience replay, transfer learning, or model-based approaches.