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