Reinforcement learning (RL) is a machine learning technique that trains models to make optimal decisions through trial and error interactions with an environment. In RL, an agent learns to navigate its environment by taking actions and receiving feedback in the form of rewards or penalties. Through this process, the agent gradually discovers which actions lead to the maximum cumulative reward and optimizes its behavior accordingly.