题目
FINTECH 540.01.Fa25 Final Exam
单项选择题
Choose all that apply to the figure below. I 𝑆 𝑡 is the current state representation of the information available to the agent. II 𝐴 𝑡 is the action decided by the external environment. III The state representation 𝑆 does not change for any reason from time t to time t+1. IV 𝐴 𝑡 is the action carried out by the agent.
选项
A.I and III
B.III and IV
C.I, II, and III
D.I and IV
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标准答案
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思路分析
The question asks which statements apply to the figure showing a typical agent–environment interaction.
Option I: 'S_t is the current state representation of the information available to the agent.' This is correct because the diagram labels S_t as the in......Login to view full explanation登录即可查看完整答案
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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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