What it is
Reinforcement learning (RL) is a type of machine learning where an "agent" learns optimal behavior by performing actions in an environment and receiving feedback in the form of rewards or penalties. The agent's goal is to learn a "policy" that dictates which action to take in any given state to maximize the cumulative reward over time. Unlike supervised learning, RL does not require labeled data; instead, it learns through trial and error, similar to how humans learn through experience.
Reinforcement learning is often highlighted in news related to AI achieving superhuman performance in complex games like chess or Go, and in the development of autonomous systems. It is crucial for training AI agents in simulations before deployment in the real world, such as robotaxis or humanoid robots. Investors track companies leveraging RL for tasks requiring sequential decision-making, as it can lead to highly efficient and adaptive AI solutions in robotics, logistics, and trading.
Why it matters
RL is critical for AI in dynamic environments, impacting autonomous systems, robotics, and complex decision-making processes across industries.
Reviewed under editorial standardsUpdated September 26, 2026Not investment advice