Modellfrei Verstärkendes Lernen (MFRL) is a branch of maschinellem Lernen that focuses on training an agent to make decisions by learning from its experiences in an environment, rather than relying on a predefined model of that environment. In contrast to model-based approaches, where the agent creates a model to predict outcomes of its actions, model-free methods allow the agent to directly learn the best actions to take based on the rewards it receives.
In MFRL umfasst der Lernprozess typischerweise zwei Hauptkomponenten: exploration and exploitation. Exploration refers to the agent trying out different actions to discover their effects, while exploitation involves the agent choosing the actions it believes will yield the highest reward based on its current knowledge. Balancing these two aspects is crucial for effective learning.
Zwei beliebte Arten des modellfreien Reinforcement Learning algorithms sind:
- Wertbasierte Methoden: These methods estimate the value of being in a given state or taking a particular action. A well-known example is Q-Learning, where the agent learns the value of actions in various states to eventually select the best action.
- Policy-basierte Methoden: Instead of estimating values, these methods directly learn a policy that specifies the best action to take in each state. An example is the REINFORCE algorithm, which uses gradients to optimize the policy.
Model-free reinforcement learning has been successfully applied in various domains, including robotics, game playing, and autonomen Systemen verwendet wird, showcasing its ability to learn complex tasks through trial and error.