What is the "advantage," typically denoted A(s,a), used for in Actor-Critic?

Question

BlackTom AI · Accepted Answer

In the context of Actor-Critic methods, the term 'advantage' is used to quantify how good a specific action is compared to the average action at that state.
Option 1: It measures how much better an action is than the average at that state. This aligns with the standard definition A(s,a) = Q(s,a) - V(s) (or equivalently A(s,a) = G_t - V(s) in some formulations), which captures the relative improvement of choosing action a in state s over the expected value of being in state s.
Option 2: It equals the value function minus the reward. This misstates the relationship; the advantage is not simply V(s) minus the immediate reward, and it does not directly subtract the reward from the value function.
Option 3: It estimates the return from a state only. That describes the value function V(s) or the state value, not the advantage, which incorporates the action and its relative quality.
Option 4: It equals the TD error plus entropy. The TD error is associated with temporal-difference learning and value updates, while entropy is a separate regularization term; neither constitutes the definition of advantage A(s,a).
Thus, the correct interpretation is that the advantage measures how much better an action is than the average at that state, guiding the policy toward actions with higher relative value.

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What is the "advantage," typically denoted A(s,a), used for in Actor-Critic?

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