Reward Function

A reward function is the core element that determines what a decision-making system is actually optimizing. In reinforcement learning, the agent does not directly “understand” the intended goal; instead, it tries to improve the signals it receives from the reward function. That means the better the reward is defined, the better the behavior aligns with the intended objective. However, there is a subtle challenge here: a poorly designed reward may create behavior that looks technically successful while being practically undesirable. For that reason, the reward function is not just a mathematical component; it is the bridge between product intent, ethical limits, operational goals, and technical optimization.