Imitation Learning, by definition, falls into a category of Transfer Learning (TL). We can define Transfer Learning as the process by which an agent or DL network is trained by transference of experiences from one to the other. This could be as simple as the observation training we just performed, or as complex as swapping layers/layer weights in an agent's brain, or just training an agent on a similar task.
Intransfer learningwe need to make sure the experiences or previous weights we use are generalized. Through the foundational chapters in this book (chapters 1-3), we learned the value of generalization using techniques such as dropout and batch normalization. We learned that these techniques are important for more general training; the form of training that allows the agent/network better inference on test data. This is no different than if we were...
