Reward Function

A reward function is a critical component in reinforcement learning that defines the objective of the learning agent. It provides feedback to the agent by assigning a numerical value to each action taken in a given state, indicating how desirable that action is in achieving the overall goal. The main characteristics of a reward function include its ability to guide the agent's learning process, shaping its behavior based on the rewards it receives. Common use cases for reward functions are found in various applications, such as game playing, robotics, and autonomous systems, where the agent learns to maximize cumulative rewards over time.