题目
题目

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

多项选择题

Select all of the following methods that use bootstrapping to estimate values

选项
A.Sarsa
B.Q-learning
C.First-visit Monte Carlo estimation
D.REINFORCE (Monte Carlo policy gradient)
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标准答案
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思路分析
The question asks which methods use bootstrapping to estimate values, meaning they update value estimates by mixing in existing estimates rather than waiting for full returns. Option: Sarsa - Sarsa is an on-policy temporal-difference method that updates Q-values using the estimate from the next state-action pai......Login to view full explanation

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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)?

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 ′ ) ]  

Which statement best describes the difference between SARSA and Q-learning?

Which of the following best describes a key difference between Monte Carlo and Temporal-Difference (TD) learning?

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