### Artificial IntelligenceAIMA Exercises

This exercise considers two-player MDPs that correspond to zero-sum, turn-taking games like those in Chapter game-playing-chapter. Let the players be $A$ and $B$, and let $R(s)$ be the reward for player $A$ in state $s$. (The reward for $B$ is always equal and opposite.)
1. Let $U_A(s)$ be the utility of state $s$ when it is $A$’s turn to move in $s$, and let $U_B(s)$ be the utility of state $s$ when it is $B$’s turn to move in $s$. All rewards and utilities are calculated from $A$’s point of view (just as in a minimax game tree). Write down Bellman equations defining $U_A(s)$ and $U_B(s)$.
2. Explain how to do two-player value iteration with these equations, and define a suitable termination criterion.
3. Consider the game described in Figure line-game4-figure on page line-game4-figure. Draw the state space (rather than the game tree), showing the moves by $A$ as solid lines and moves by $B$ as dashed lines. Mark each state with $R(s)$. You will find it helpful to arrange the states $(s_A,s_B)$ on a two-dimensional grid, using $s_A$ and $s_B$ as “coordinates.”
4. Now apply two-player value iteration to solve this game, and derive the optimal policy. (a) $3 \times 3$ world for Exercise 3x3-mdp-exercise. The reward for each state is indicated. The upper right square is a terminal state. (b) $101 \times 3$ world for Exercise 101x3-mdp-exercise (omitting 93 identical columns in the middle). The start state has reward 0.

This exercise considers two-player MDPs that correspond to zero-sum, turn-taking games like those in Chapter game-playing-chapter. Let the players be $A$ and $B$, and let $R(s)$ be the reward for player $A$ in state $s$. (The reward for $B$ is always equal and opposite.)
1. Let $U_A(s)$ be the utility of state $s$ when it is $A$’s turn to move in $s$, and let $U_B(s)$ be the utility of state $s$ when it is $B$’s turn to move in $s$. All rewards and utilities are calculated from $A$’s point of view (just as in a minimax game tree). Write down Bellman equations defining $U_A(s)$ and $U_B(s)$.
2. Explain how to do two-player value iteration with these equations, and define a suitable termination criterion.
3. Consider the game described in Figure line-game4-figure on page line-game4-figure. Draw the state space (rather than the game tree), showing the moves by $A$ as solid lines and moves by $B$ as dashed lines. Mark each state with $R(s)$. You will find it helpful to arrange the states $(s_A,s_B)$ on a two-dimensional grid, using $s_A$ and $s_B$ as “coordinates.”
4. Now apply two-player value iteration to solve this game, and derive the optimal policy. (a) $3 \times 3$ world for Exercise 3x3-mdp-exercise. The reward for each state is indicated. The upper right square is a terminal state. (b) $101 \times 3$ world for Exercise 101x3-mdp-exercise (omitting 93 identical columns in the middle). The start state has reward 0.

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