The reward signal in reinforcement learning (RL) serves as the primary feedback mechanism for the agent, guiding its learning process. When an agent performs an action in a given state, the reward signal provides information about the effectiveness of that action, allowing the agent to adjust its behavior. The reward signal tells the agent whether the action taken was good or bad in achieving its goal.
The reward signal drives the agent toward optimal decision-making by reinforcing actions that result in positive outcomes and penalizing those that lead to negative outcomes. For example, in a robot navigation task, the agent may receive a reward for getting closer to the target and a penalty for hitting obstacles. This feedback helps the agent learn strategies that maximize long-term rewards.
Without the reward signal, the agent would have no way of knowing which actions are beneficial or harmful. The reward signal is therefore essential for the agent to learn and adapt its behavior to optimize future performance and achieve its goals.