In reinforcement learning, rewards serve as a critical signal that guides the agent's learning process. The agent takes actions in the environment, and based on those actions, it receives feedback in the form of rewards or penalties. The rewards indicate the immediate value of the agent’s actions, helping it adjust its policy to maximize long-term returns.
The agent’s goal is to maximize cumulative rewards over time. Positive rewards reinforce actions that lead to desired outcomes, while negative rewards or penalties discourage undesired actions. By associating certain actions with higher rewards, the agent learns which actions are beneficial and should be repeated.
Rewards are essential for the agent to understand which actions contribute to achieving the final objective and which ones should be avoided. Effective reward design is crucial to ensure that the agent learns the correct behavior and doesn't develop suboptimal strategies.