# Question

Consider an undiscounted MDP having three states, (1, 2, 3), with rewards —1, —2, 0 respectively. State 3 is a terminal stale. In states I and 2 there are two possible actions: a and b. The transition model is as follows:

• In state 1, action a moves the agent to state 2 with probability 0.8 and makes the agent stay put with probability 0.2.

• In state 2, action a moves the agent to state 1 with probability 0.8 and makes the agent stay put with probability 0.2.

• In either state 1 or state 2, action b moves the agent to stale 3 with probability 0.1 and makes the agent stay put with probability 0.9. Answer the following questions:

a. What can he determined qualitatively about the optimal policy in states 1 and 2?

b. Apply policy iteration, showing each step in full, to determine the optimal policy and the values of states 1 and 2. Assume that the initial policy has action bin both states.

c. What happens to policy iteration if the initial policy has action a in both stales? Does discounting help? Does the optimal policy depend on the discount factor?

• In state 1, action a moves the agent to state 2 with probability 0.8 and makes the agent stay put with probability 0.2.

• In state 2, action a moves the agent to state 1 with probability 0.8 and makes the agent stay put with probability 0.2.

• In either state 1 or state 2, action b moves the agent to stale 3 with probability 0.1 and makes the agent stay put with probability 0.9. Answer the following questions:

a. What can he determined qualitatively about the optimal policy in states 1 and 2?

b. Apply policy iteration, showing each step in full, to determine the optimal policy and the values of states 1 and 2. Assume that the initial policy has action bin both states.

c. What happens to policy iteration if the initial policy has action a in both stales? Does discounting help? Does the optimal policy depend on the discount factor?

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