Well so you’re obviously pretraining using imitation learning, so I’ve got that part down.
If I understand your post right, the rest of the policy training is done by policy gradients on human-induced rewards? As I understand it, policy gradient is close to a macimally sample-hungry method, because it does not do any modelling. At one level I would class this as random exploration, but on another level the humans are allowed to provide reinforcement based on methods rather than results, so I suppose this also gives it an element of imitation learning.
So I guess my expectation is that your training method is too sample inefficient to achieve much beyond human imitation.
What about this post?
Well so you’re obviously pretraining using imitation learning, so I’ve got that part down.
If I understand your post right, the rest of the policy training is done by policy gradients on human-induced rewards? As I understand it, policy gradient is close to a macimally sample-hungry method, because it does not do any modelling. At one level I would class this as random exploration, but on another level the humans are allowed to provide reinforcement based on methods rather than results, so I suppose this also gives it an element of imitation learning.
So I guess my expectation is that your training method is too sample inefficient to achieve much beyond human imitation.