What is a reward function?
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In reinforcement learning (RL), a reward function is a key component that guides an agent’s behavior by providing feedback on its actions. It is a mathematical function that assigns a numerical value (reward) to each action or state, representing how good or bad that action is in achieving the overall goal. The agent uses these rewards to learn which actions lead to long-term success.
For example, in a game, scoring points can be considered positive rewards, while losing a life may be a negative reward (penalty). In a robot navigation task, reaching the destination may give a high positive reward, while hitting an obstacle may give a negative one. The agent’s objective is to maximize the cumulative reward (also called return) over time.
Formally, the reward function is part of a Markov Decision Process (MDP), often denoted as R(s, a) or R(s, a, s'), where it evaluates the reward for taking action a in state s and possibly transitioning to state s'. Unlike humans, agents don’t inherently know what is “good” or “bad” — the reward function defines this.
In short, the reward function is the goal signal for the agent, shaping its learning and decision-making process. Designing it carefully is crucial, because poorly defined rewards may cause unintended or undesirable behaviors.
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