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Analyzing Credit Assignment for a Policy Update
Consider an agent's action, a₂, taken at time step t=2 within a 4-step process. When updating the agent's decision-making strategy, we need to assign a 'quality score' to this action to determine if it should be encouraged or discouraged. Based on the provided scenario:
- Calculate the total reward for the entire sequence.
- Calculate the sum of rewards from time step
t=2onward. - Argue which of these two values provides a more accurate and effective signal for updating the policy for action
a₂. Justify your reasoning by considering which rewards the actiona₂could have plausibly influenced.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
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An agent completes a task, which consists of a sequence of states, actions, and rewards
(s_1, a_1, r_1), (s_2, a_2, r_2), ..., (s_T, a_T, r_T). To improve the agent's performance, we need to adjust the likelihood of taking each actiona_tat states_t. Consider two different ways to calculate the 'quality score' used to update the actiona_t:- Method 1: The score is the sum of all rewards in the sequence:
r_1 + r_2 + ... + r_T. - Method 2: The score is the sum of rewards from time step
tonward:r_t + r_{t+1} + ... + r_T.
Which of the following statements best explains why Method 2 is generally a more effective approach for training the agent than Method 1?
- Method 1: The score is the sum of all rewards in the sequence:
An agent completes an episode of 4 time steps, receiving the following sequence of rewards:
r_1 = -10,r_2 = +2,r_3 = +5,r_4 = -1. When updating the agent's decision-making process, what is the 'reward-to-go' value that should be associated with the action taken at time stept=2?Analyzing Credit Assignment for a Policy Update