Machine Learning, Optimization, Gradient Descent, Reinforcement Learning.
your model outputs a multinoulli distribution of actions with probabilities: Note in practice the π’s would be represented using a vector. In supervised learning, you’re given a label π* (represented as a 1-hot vector) and the model learns by making the predictions π_1, π_2, … as close to π* as