A soccer robot R is on a fast break toward the goal, starting in position 1. From positions 1 through 3, it can either shoot (S) or dribble the ball forward (D). From 4 it can only shoot. If it shoots, it either scores a goal (state G) or misses (state M). If it dribbles, it either advances a square or loses the ball, ending up in state M. R 1 2 3 P(G|k, S) In this Markov Decision Process (MDP), the states are 1, 2, 3, 4, G, and M, where G and M are terminal states. The transition model depends on the parameter y, which is the probability of dribbling successfully (i.e., advancing a square). Assume a discount of 71. For k {1,2,3,4}, we have = 6 and rewards are 0 for all other transitions. 4 P(M|k, S) = 1 - P(k+1|k, D) = y k 6 P(M|k, D) = 1-y, R(k, S, G) = 1 Goal (a) (3 points) Denote by V" the value function for the specific policy T. What is V" (1) for the policy that always shoots? (b) (4 points) Denote by Q'(s. a) the value of a q-state (s, a), which is the expected utility when starting with action a at states, and thereafter acting optimally. What is Q'(3. D) in terms of y? (c) (5 points) Denote by V (s) the value of a state s at iteration t, which is the expected utility when starting in states and acting optimally. Using y, complete the first two iterations (t = 1.2) of value iteration. Iteration 0 corresponds to having value 0 in every state: V(1) = V(2) = V(3) = V(4) = 0. Hint: Recall that V1(s) = max P(s's, a) (R(s. a, s') + V (s')). aEA (d) (3 points) For what range of values of y is Q' (3, S) 2 Q*(3, D)?