belønningsfunksjon

A belønningsfunksjon, often translated as reward function, is a core concept in reinforcement learning. It is a mechanism that assigns a numerical value to a given state or action within an environment. This value, or reward, guides the learning agent's behavior. The primary goal of the agent is to maximize the cumulative reward it receives over time.

The reward function defines the objective of the reinforcement learning problem. A well-designed reward function encourages the agent to learn desirable behaviors and avoid undesirable ones. For example, in a game-playing agent, a positive reward might be given for scoring points, while a negative reward (or penalty) could be given for losing a life. The agent's learning algorithm, such as Q-learning or policy gradients, uses these rewards to update its internal policy, which dictates its actions in different states.

The design of the reward function is crucial and can be challenging. Sparse rewards, where positive feedback is infrequent, can make learning difficult. Conversely, overly dense rewards can sometimes lead to unintended behaviors if the agent learns to exploit loopholes in the reward system. The reward function is context-dependent and must be carefully tailored to the specific problem being solved. It acts as the objective signal that shapes the agent's exploration and exploitation strategies.