In an MDP (Markov Decision Process), the following statements are true:
The optimal policy for a given state s is unique.
The problem of determining the value of a state is solved recursively by the value iteration algorithm.
The optimal policy for a given state in an MDP refers to the best course of action to take from that state in order to maximize expected rewards or outcomes. This policy is unique because, given a specific state, there is a single action or set of actions that yields the highest expected value.
The value iteration algorithm is a dynamic programming method used to determine the value of each state in an MDP. It starts with an initial estimate of the state values and then iteratively updates them until convergence. This recursive process involves considering the immediate rewards and expected future rewards obtained by transitioning from one state to another, following the optimal policy. Through this algorithm, the values of states are refined and converge to their optimal values.
The third statement, "V* (s) = 25, T (s, a, s') [R (s, a, s') + yV* (s')]," represents the equation for calculating the value function V*(s) of each state in an MDP. It states that the value of a state is determined based on the transition probabilities T(s, a, s'), immediate rewards R(s, a, s'), discount factor y, and the value of the next state V*(s'). This equation allows us to compute the value of a state by considering the expected rewards and future values.
The fourth statement, "Q* (s, a) = ∑T (s, a, s') [R (s, a, s') + yV* (s')]," represents the equation for calculating the action-value function Q*(s, a) in an MDP. It calculates the expected value of taking action a in state s, considering the transition probabilities, immediate rewards, discount factor, and the value of the next state. However, the specific notation given in the statement, with "2,," is incomplete or incorrect, making it an invalid equation.
In summary, the optimal policy for a given state in an MDP is unique, and the value of each state is determined recursively using the value iteration algorithm. The value function V*(s) and the action-value function Q*(s, a) play key roles in evaluating the expected rewards and future values in an MDP.