Reinforcement Learning teaches machines through trial and error. Agents take actions, receive rewards or penalties, and gradually learn optimal behavior based on experience and feedback loops.
An RL agent interacts with its environment, refining its strategy to maximize cumulative rewards. This dynamic approach allows machines to learn and adapt in real-time, much like humans do.
From robotics and game AI to finance and autonomous vehicles, reinforcement learning powers systems that think, adapt, and improve with each decision made in uncertain conditions.
As RL advances, it promises smarter AI capable of independent decision-making. With greater computational power, we’ll unlock deeper levels of autonomy in machines and real-world problem-solving.