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Artificial Intelligence

What is Reinforcement Learning?

Quick answer

A type of machine learning in which an agent learns by trial and error, receiving rewards or penalties for its actions.

Reinforcement Learning, explained

In reinforcement learning, an agent interacts with an environment, takes actions, and receives feedback in the form of rewards. Over many attempts, it learns a strategy, called a policy, that maximizes total reward over time. No labeled answers are given; the agent discovers what works.

Famous successes include systems that learned to play Atari video games and DeepMind's AlphaGo, which defeated a top professional Go player in 2016. Reinforcement learning is also used in robotics, recommendation systems, and resource management. A variation called reinforcement learning from human feedback is used to make large language models more helpful and safer, with human ratings guiding the reward. Challenges include designing good reward signals and the large number of trials often needed.

Related terms

AI Agent

A software system that uses an AI model to plan and carry out multi-step tasks, often by calling tools, APIs, or other programs on its own.

Machine Learning

A branch of AI in which computers learn patterns from data to make predictions or decisions without being explicitly programmed for each case.

Reinforcement Learning from Human Feedback

A training method, known as RLHF, that improves an AI model by rewarding responses people rate as more helpful, accurate, or safe.

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