How can value iteration be used to solve an MDP?
Value iteration is a method used to solve an MDP by iteratively improving the value function until it converges. The value function is a mapping from states to rewards, and it represents the expected return from a given state. The value iteration algorithm works by starting with an initial value function and then repeatedly updating it according to the Bellman equation. The algorithm converges when the value function converges to the true value function of the MDP.
