Tugevdusõppe

Tugevdusõppe, also known as reinforcement learning, is a machine learning paradigm concerned with how software agents ought to take actions in an environment in order to maximize some notion of cumulative reward. It is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

In reinforcement learning, the agent learns by interacting with its environment. It receives feedback in the form of rewards or penalties based on the actions it takes. The goal of the agent is to learn a policy, which is a strategy that dictates what action to take in any given state, that maximizes the expected cumulative reward over time.

Key components of a reinforcement learning system include 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. An action is a choice made by the agent. The reward is a scalar value that indicates how good or bad an action was.

Reinforcement learning has been applied to a wide range of problems, including robotics, game playing, autonomous driving, and personalized recommendations. Famous examples include AlphaGo, which defeated a human champion in the game of Go, and systems that learn to control complex industrial processes. The field is actively researched, with ongoing work on improving sample efficiency, handling complex state and action spaces, and ensuring safety and robustness of learned policies.