If the actor to train and the actor for interacting is the same, we call it on-policy. In other words, if the actor himself does training to gain experience, it is on policy; if the actor gains experience by watching other actors train, it is off-policy.
Reinforcement learning is unsupervised and it will solve the problem there is no standard answer(or humans also don't know the best way to solve).
All the purpose of the machine learning problem is to find a function, reinforcement learning is no exception.
There are 200 students in the three departments (100 in Department A, 60 in Department B, 40 in Department C), and a total of 20 representative meetings are allocated proportionally. The three departments have 10, 6, and 4 seats respectively:
Deep learning models can handle semantics by vectorizing natural language.
When reading long articles, your focus naturally shifts from one word to another, depending on the context.
The mechanism mimic this behavior, allowing models to selectively concentrate on specific elements of the input data while ignoring others.
