Tugevdamisõppi

Tugevdamisõppi, often translated as reinforcement learning, is a machine learning paradigm where an agent learns to make a sequence of decisions by trial and error. The agent interacts with an environment, taking actions and receiving feedback in the form of rewards or penalties. The goal of the agent is to maximize its cumulative reward over time.

Unlike supervised learning, where the algorithm is given labeled examples, or unsupervised learning, which seeks to find patterns in unlabeled data, reinforcement learning operates on the principle of learning from experience. The agent does not need explicit instructions on which action to take, but rather discovers optimal strategies by exploring the environment and observing the consequences of its actions.

Key components of reinforcement learning include the agent, the environment, states, actions, and rewards. The agent perceives the current state of the environment, chooses an action to perform, and transitions to a new state. This transition is accompanied by a reward signal, which indicates how beneficial or detrimental the action was in that particular state.

Common algorithms in reinforcement learning include Q-learning, SARSA, and policy gradient methods. These algorithms differ in how they represent and update the agent's knowledge about the optimal actions to take in different states. The ultimate aim is to learn a policy, which is a function that maps states to actions, leading to the maximization of expected future rewards. Reinforcement learning has applications in various fields, including robotics, game playing, autonomous driving, and recommendation systems.