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COMP90054_2025_SM2 Supplementary or Special Exam: AI Planning for Autonomy (COMP90054_2025_SM2)- Requires Respondus LockDown Browser

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The value of an action 𝑞 𝜋 ( 𝑠 , 𝑎 ) depends on the expected next reward and the expected value of the next state.  We can think of this in terms of a small backup diagram, as follows: Let 𝑃 ( 𝑠 ′ | 𝑠 , 𝑎 ) be the transition probability and 𝑟 ¯ ( 𝑠 , 𝑎 , 𝑠 ′ ) = 𝐸 [ 𝑅 𝑡 + 1 | 𝑆 𝑡 = 𝑠 , 𝐴 𝑡 = 𝑎 , 𝑆 𝑡 + 1 = 𝑠 ′ ] the expected reward for the transion from state 𝑠 to state 𝑠 ′ via action 𝑎 . Rearrange the definition of 𝑞 𝜋 ( 𝑠 , 𝑎 ) in terms of these quantities, such that no expected-value notation appears in the equation. A.   𝑞 𝜋 ( 𝑠 , 𝑎 ) = ∑ 𝑠 ′ 𝑃 ( 𝑠 ′ ∣ 𝑠 , 𝑎 ) [ 𝑟 ¯ ( 𝑠 , 𝑎 , 𝑠 ′ ) + 𝛾 𝑞 𝜋 ( 𝑠 ′ , 𝑎 ) ] B.     𝑞 𝜋 ( 𝑠 , 𝑎 ) = ∑ 𝑠 ′ [ 𝑟 ¯ ( 𝑠 , 𝑎 , 𝑠 ′ ) + 𝛾 ] 𝑃 ( 𝑠 ′ ∣ 𝑠 , 𝑎 ) 𝑣 𝜋 ( 𝑠 ′ ) C.     𝑞 𝜋 ( 𝑠 , 𝑎 ) = ∑ 𝑠 ′ 𝑃 ( 𝑠 ′ | 𝑠 , 𝑎 ) [ 𝑟 ¯ ( 𝑠 , 𝑎 , 𝑠 ′ ) + 𝛾 𝑣 𝜋 ( 𝑠 ′ ) ] D.   𝑞 𝜋 ( 𝑠 , 𝑎 ) = 𝑃 [ 𝑠 ′ ∣ 𝑠 , 𝑎 ] [ 𝑟 ¯ ( 𝑠 , 𝑎 , 𝑠 ′ ) + 𝛾 𝑣 𝜋 ( 𝑠 ′ ) ]  

Options
A.D
B.B
C.A
D.C
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Step-by-Step Analysis
We begin by identifying the underlyingBellman equation for action values under policy π. The quantity qπ(s, a) is the expected return when taking action a in state s and thereafter following policy π, which can be written as an expectation over next states s' of the immediate reward plus the discounted future value from the next state when continuing with the same action a (since the action chosen at state s is fixed to a). Now we evaluate each option. ......Login to view full explanation

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