In this chapter, we've learned about Meta-SGD and the Reptile algorithm. We saw how Meta-SGD differs from MAML and how Meta-SGD is used in supervised and reinforcement learning settings. We saw how Meta-SGD learns the model parameter along with learning rate and update direction. We also saw how to build Meta-SGD from scratch. Then, we learned about the Reptile algorithm. We saw how Reptile differs from MAML and how Reptile acts as an improvement over the MAML algorithm. We also learned how to use Reptile in a sine wave regression task.
In the next chapter, we'll learn how we can use gradient agreement as an optimization objective in meta learning.
