In Reinforcement Learning, the Monte Carlo method is primarily used for:

Group of answer choices

Finding the optimal policy directly by gradient descent methods.

Estimating transition probabilities using a model$-$based approach.

Learning the value function or policy through experience by averaging returns from multiple episodes

Computing the exact value of state$-$action pairs by solving Bellman equations.