belønningsfunksjonen

Belønningsfunksjonen, often translated as the reward function, is a core concept in reinforcement learning. It defines the goal of an agent by assigning a scalar value, the reward, to each state or state-action pair. The agent's objective is to maximize the cumulative reward it receives over time. A positive reward indicates a desirable outcome, while a negative reward, or penalty, signifies an undesirable one.

The design of the reward function is crucial for successful reinforcement learning. A well-designed reward function clearly guides the agent towards the desired behavior, whereas a poorly designed one can lead to unintended consequences or suboptimal performance. For instance, in a game-playing agent, a reward might be given for scoring points and a penalty for losing a life. In robotics, a reward could be associated with reaching a target location, while a penalty might be given for colliding with obstacles.

The reward signal is typically received by the agent after each action it takes. This feedback loop allows the agent to learn which actions lead to higher cumulative rewards through trial and error. The agent adjusts its policy, which is its strategy for choosing actions, based on the rewards it receives. Over time, the agent aims to discover an optimal policy that maximizes its expected long-term reward. The reward function itself is not learned by the agent but is provided as part of the problem definition.