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Advantage Function Estimation using Reward-to-Go
The advantage at a time step t, denoted as , quantifies the relative benefit of taking a specific action compared to the expected value of following the policy from state onward. It can be estimated by subtracting a baseline from the actual return. Using the state-value function as the baseline, the formula is: In this equation, the term represents the actual return received from time step , while represents the expected return from state .
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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
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Policy Gradient Reformulation using Advantage Function
Advantage Function Estimation using Reward-to-Go
An autonomous agent in a reinforcement learning environment is in a particular state. From this state, the expected cumulative future reward, when averaged across all possible actions, is calculated to be 50 points. The agent is evaluating three specific actions:
- Action X: The expected cumulative reward for taking this action is 65 points.
- Action Y: The expected cumulative reward for taking this action is 40 points.
- Action Z: The expected cumulative reward for taking this action is 50 points.
Based on this information, which statement provides the most accurate analysis for guiding the agent's next policy update?
In a reinforcement learning scenario, an agent in a specific state calculates that the 'advantage' of performing a particular action is exactly zero. What is the most accurate interpretation of this finding?
Temporal Difference (TD) Error as an Advantage Function Estimator
Analysis of an Agent's Suboptimal Policy
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Policy Gradient with Reward-to-Go and Baseline
Calculating Advantage from a Trajectory
In the context of estimating the advantage of taking an action
a_tin a states_t, the formulaA(s_t, a_t) = (∑_{k=t}^{T} r_k) - V(s_t)is often used. What is the primary role of the reward-to-go term,∑_{k=t}^{T} r_k, within this specific estimation?In a given trajectory, if the calculated advantage
A(s_t, a_t) = (∑_{k=t}^{T} r_k) - V(s_t)is negative, it implies that the actiona_ttaken in states_tled to a sequence of rewards that was worse than the average expected outcome from that state.