What is a Reward Function?
In the context of Reinforcement Learning, a reward function is a critical component that defines the objective of an agent within an environment. The reward function provides feedback to the agent based on its actions; it assigns a scalar value (reward) to each action taken in a given state. This value is used to determine the success or desirability of the action in relation to achieving the agent's goal.
The primary purpose of the reward function is to guide the agent's learning process. When the agent performs actions that are deemed favorable, it receives positive rewards, reinforcing those behaviors. Conversely, negative rewards (or penalties) are given for undesirable actions, prompting the agent to avoid them in the future. Over time, the agent learns to maximize the cumulative reward by exploring various actions and their outcomes.
Designing an effective reward function is crucial, as it directly influences the learning efficiency and behavior of the agent. A well-structured reward function can accelerate learning, ensuring the agent exhibits desired behaviors. On the other hand, poorly defined rewards can lead to unintended consequences, where the agent may exploit loopholes in the reward structure rather than achieving the intended goals.