Optimizing Gradient Calculation in a Learning Agent
When updating an agent's decision-making process (its policy) based on the action taken at step t=5, the learning algorithm calculates a gradient. This calculation considers the total reward obtained. Based on the principle that an action can only influence subsequent outcomes, which portion of the total reward sum is irrelevant to the gradient calculation for the action at t=5? Explain your reasoning.
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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
Application in Bloom's Taxonomy
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Sum of Past Rewards Notation
Optimizing Gradient Calculation in a Learning Agent
In the derivation of a policy gradient algorithm, we aim to update a policy based on actions taken within an episode. A core principle states that an action taken at a specific time step, , can only influence rewards received from that point forward (). Given this principle, which of the following mathematical expressions correctly identifies the reward term that should be used to scale the gradient update for the action at time step ?
Justification for Policy Gradient Simplification