An AI agent uses Q-learning algorithm with a = 0.85, y = 0.6, 012345 r =...

  

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An AI agent uses Q-learning algorithm with a = 0.85, y = 0.6, 012345 r = 2 0 0 0 0 1 LO 1 0 0 0 0 1 0 0 0 1 0 1 1 0 1 0 0 1 0 1 0 0 1 2 3 4 5 0 1 0 10 0 1 0 and Q = 2 0 3 10 10 4 5 0 0 0 0 0 6.88 0 1 0 0 0 8.55 0 11.32 2 0 0 0 4.58 0 0 3 0 6.76 0 6.91 0 2.77 0 4 9.86 0 9.85 0 9.63 5 0 21.54 0 0 21.06 20.28 Assume the agent has finished learning and obtained the Q matrix is as shown above. If the current state of the agent is 4, then the maximum expected reward it can achieve is: O 30.71 31.06 21.06 12 10 An AI agent uses Q-learning algorithm with a = 0.85, y = 0.6, 012345 r = 2 0 0 0 0 1 LO 1 0 0 0 0 1 0 0 0 1 0 1 1 0 1 0 0 1 0 1 0 0 1 2 3 4 5 0 1 0 10 0 1 0 and Q = 2 0 3 10 10 4 5 0 0 0 0 0 6.88 0 1 0 0 0 8.55 0 11.32 2 0 0 0 4.58 0 0 3 0 6.76 0 6.91 0 2.77 0 4 9.86 0 9.85 0 9.63 5 0 21.54 0 0 21.06 20.28 Assume the agent has finished learning and obtained the Q matrix is as shown above. If the current state of the agent is 4, then the maximum expected reward it can achieve is: O 30.71 31.06 21.06 12 10

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