In the framework of reinforcement learning, what does maximizing reward imply?

Maximizing reward in the context of reinforcement learning means making decisions that lead to the best outcomes. In reinforcement learning, an agent learns to make choices in an environment to achieve the maximum cumulative reward over time. This process is guided by the principle that actions leading to higher rewards are more favorable, and thus the agent aims to identify and select those actions that lead to the most advantageous results in terms of the feedback it receives.

For instance, if an agent is navigating a maze, the goal is to find the exit by taking actions that provide positive feedback (rewards) and avoiding actions that would lead to negative feedback (penalties). Over time, through exploration and learning, the agent refines its strategy to consistently select the best actions that yield the highest cumulative rewards, demonstrating the essence of effective decision-making within reinforcement learning.