What Is Reinforcement Learning?
Reinforcement learning (RL) is a machine learning paradigm where an agent learns to make optimal decisions by interacting with an environment, receiving rewards for desirable actions and penalties for undesirable ones, gradually improving its strategy over time.
How Reinforcement Learning Works
In reinforcement learning, an agent observes the current state of its environment, takes an action, receives a reward signal, and then updates its policy (decision-making strategy) to maximize future rewards. Unlike supervised learning, RL does not require labeled data — the agent learns through trial and error. This approach is inspired by how humans and animals learn from experience. RL has achieved superhuman performance in games like Go and chess, and it is the backbone of RLHF used to align language models. It is also used in robotics, autonomous vehicles, and resource optimization.
Real-World Examples
DeepMind's AlphaGo learning to play Go at a superhuman level by playing millions of games against itself
A robotic arm learning to grasp objects through thousands of trial-and-error attempts in a simulated environment
An AI system optimizing energy consumption in a data center by learning which cooling adjustments save the most power