Objective Function as Expected Cumulative Reward (Performance Function)

An agent is being trained to find the best route through a system. It is presented with two options:

• Route 1: Provides a consistent, small positive reward at every step, resulting in a total reward of +15 for the entire route.
• Route 2: Starts with a step that gives a negative reward (a penalty) of -5, but subsequent steps lead to very high rewards, resulting in a total reward of +50 for the entire route.

An agent that has been successfully trained according to the primary objec

Analysis of a Suboptimal Agent Policy

An agent is learning to play a game where the objective is to get the highest possible final score. At a critical decision point, the agent chooses an action that yields an immediate reward of 0, passing up an alternative action that would have given an immediate reward of +10. This decision is necessarily an indication that the agent's policy is flawed and not aligned with the primary goal of its learning framework.