Belohnungslernen

Belohnungslernen, also known as Reinforcement Learning (RL) in English, is a machine learning paradigm where an agent learns to make decisions by taking actions in an environment to maximize a cumulative reward. Unlike supervised learning, which relies on labeled data, or unsupervised learning, which seeks patterns in unlabeled data, RL operates on a trial-and-error basis. The agent interacts with its environment, receives feedback in the form of rewards or penalties, and adjusts its behavior over time to achieve its goals.

The core components of a reinforcement learning system include an agent, an environment, a state, an action, and a reward. The agent observes the current state of the environment, chooses an action based on its learned policy, and transitions to a new state. The environment then provides a reward signal, which can be positive for desirable actions or negative for undesirable ones. The agent's objective is to learn a policy that maximizes the expected sum of future rewards.

Key concepts in RL include value functions, which estimate the expected future reward from a given state or state-action pair, and policies, which define the agent's behavior by mapping states to actions. Algorithms like Q-learning and Deep Q-Networks (DQN) are widely used to learn these policies and value functions. RL has found applications in various fields, including robotics, game playing (e.g., AlphaGo), autonomous driving, and resource management. Its ability to learn complex strategies in dynamic environments makes it a powerful tool for solving a wide range of decision-making problems.