What is Reinforcement Learning?
Also known as: RL
Reinforcement Learning (RL) is a machine learning method where an agent learns to make sequential decisions by interacting with an environment, receiving rewards or penalties, and aiming to maximize its long-term reward.
In RL, an agent observes the current state of the environment, chooses an action, and receives feedback in the form of a reward signal. Over many interactions, it updates its strategy (called a policy) to favor actions that lead to higher cumulative rewards.
Key ideas include trial-and-error learning, balancing exploration of new actions with exploitation of known good actions, and concepts like states, actions, rewards, and value functions that help estimate future success.
Unlike other learning types, RL does not require labeled data; the agent discovers effective behavior through direct experience and delayed feedback.
Example
A robot learning to walk: it tries different leg movements, falls over (negative reward), or stays upright longer (positive reward), gradually improving its walking policy through repeated trials.
Why it matters
RL powers breakthroughs in game-playing AI, robotics, autonomous systems, and optimization problems where decisions must be made over time without explicit instructions.
Frequently asked questions
Supervised learning uses labeled examples to learn mappings, while RL learns from rewards and punishments through interaction without needing labeled data.
Related terms
Supervised learning is a machine learning method where a model is trained on data that already has correct answers attached, so it can learn to predict those answers for new data.
Adam (Adaptive Moment Estimation) is a popular optimization algorithm used to train machine learning models by iteratively updating parameters based on gradients.
Classification is a supervised machine learning task that assigns input data to one of several predefined categories or classes based on patterns learned from labeled training examples.
Clustering is an unsupervised machine learning technique that automatically groups similar data points together into clusters based on their features, without using any labeled examples.
Gradient descent is an optimization algorithm that finds the minimum of a function by repeatedly moving in the direction of the steepest downward slope. In machine learning it is used to minimize a model's error by adjusting parameters step by step.
A hyperparameter is a value or setting chosen by the user before training a machine learning model that controls the learning process itself.