An agent in a sequential decision-making process is at time step 't' and needs to select an action. The agent's goal is to choose actions that maximize the sum of all future rewards. Given that the agent has already received rewards for all actions taken up to this point, how should the quantity represented by the expression $\sum_{k=1}^{t-1} r_k$ be considered when determining the optimal action at the current time step 't'?

In the context of optimizing an agent's behavior at a specific time step t, the quantity represented by the expression $\sum_{k=1}^{t-1} r_k$ is considered a variable that directly influences the update direction for the agent's current decision.

Calculating Cumulative Past Rewards