Forsterkningslæring

Forsterkningslæring, also known as reinforcement learning in English, is a subfield of machine learning concerned with how agents ought to take actions in an environment to maximize some notion of cumulative reward. It is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. In forsterkningslæring, an agent learns to make decisions by trial and error. The agent interacts with its environment by performing actions, and for each action, it receives a reward or penalty. The goal of the agent is to learn a policy, which is a mapping from states to actions, that maximizes the expected cumulative reward over time.

The core components of a forsterkningslæring system are the agent, the environment, the state, the action, and the reward. The agent is the learner and decision-maker. The environment is everything the agent interacts with. The state represents the current situation of the environment. The action is what the agent does. The reward is a scalar feedback signal that indicates how good an action was in a particular state.

Key concepts in forsterkningslæring include exploration versus exploitation, value functions, and policies. Exploration involves trying out new actions to discover potentially better rewards, while exploitation involves choosing actions that are known to yield high rewards. Value functions estimate the expected future reward from a given state or state-action pair. Policies define how the agent behaves by specifying which action to take in each state.

Forsterkningslæring has been applied to a wide range of problems, including game playing (e.g., AlphaGo), robotics, autonomous driving, and recommendation systems. Its ability to learn from interaction makes it suitable for tasks where explicit programming of optimal behavior is difficult or impossible.