The reward function in reinforcement learning (RL) is a mathematical function that defines the feedback an agent receives after taking an action in a particular state. It maps the state-action pair to a numerical value, which can be positive (reward), negative (penalty), or zero, indicating how favorable or unfavorable the action was in that state. The reward function is essential because it guides the agent’s learning process, helping the agent understand which actions lead to desirable outcomes.
In RL, the goal is for the agent to maximize its cumulative reward over time by selecting actions that yield high rewards. The reward function influences the agent’s behavior by assigning values to states and actions. For instance, in a game, the agent might receive a positive reward for scoring points and a negative reward for losing a life.
The design of the reward function is critical because it shapes the agent’s learning. If the reward function is too sparse or poorly defined, the agent may struggle to learn an effective policy. It must be carefully crafted to reflect the desired objectives of the task or environment.