题目
COMP90054_2025_SM2 Supplementary or Special Exam: AI Planning for Autonomy (COMP90054_2025_SM2)- Requires Respondus LockDown Browser
单项选择题
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 ′ ) ]
选项
A.D
B.B
C.A
D.C

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标准答案
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思路分析
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.
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类似问题
Shown is the Q Actor-Critic (QAC) function, with line numbers. 1. Initialise 𝑠 , 𝜃 2. Sample 𝑎 ∼ 𝜋 𝜃 3. for each step do 4. Sample reward 𝑟 = 𝑅 𝑠 𝑎 ; sample transition 𝑠 ′ ∼ 𝑃 𝑠 , ⋅ 𝑎 5. Sample action 𝑎 ′ ∼ 𝜋 𝜃 ( 𝑠 ′ , 𝑎 ′ ) 6. 𝛿 = 𝑟 + 𝛾 𝑄 𝑤 ( 𝑠 ′ , 𝑎 ′ ) − 𝑄 𝑤 ( 𝑠 , 𝑎 ) 7. 𝜃 ← 𝜃 + 𝛼 ∇ 𝜃 𝑙 𝑜 𝑔 𝜋 𝜃 ( 𝑠 , 𝑎 ) 𝑄 𝑤 ( 𝑠 , 𝑎 ) 8. 𝑤 ← 𝑤 + 𝛽 𝛿 𝜙 ( 𝑠 , 𝑎 ) 9. 𝑎 ← 𝑎 ′ , 𝑠 ← 𝑠 ′ 10. end for Which of the following statements is true (can be more than one)?
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