tugevdamisõppe

Tugevdamisõppe, often translated as reinforcement learning in English, is a type of machine learning 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, reinforcement learning operates through trial and error. The agent interacts with its environment, observes the outcomes of its actions, and receives feedback in the form of rewards or penalties. The goal is for the agent to discover a strategy, known as a policy, that leads to the highest possible total reward over time.

The core components of a reinforcement learning system include an agent, an environment, a state, an action, and a reward. The agent perceives the current state of the environment and chooses an action to perform. This action changes the environment's state, and the agent receives a reward signal that indicates the desirability of that transition. Over many interactions, the agent learns to associate states with actions that yield positive rewards and avoid actions that lead to negative rewards.

Common algorithms in reinforcement learning include Q-learning, SARSA, and policy gradient methods. These algorithms differ in how they estimate the value of states or state-action pairs and how they update the agent's policy. Applications of reinforcement learning are diverse, ranging from robotics and game playing (like AlphaGo) to recommendation systems and autonomous driving. The learning process can be computationally intensive, and challenges include exploration versus exploitation – balancing trying new actions to discover better strategies with exploiting known good actions.