What is Reinforcement Learning?
Reinforcement learning is a machine learning paradigm where an AI agent learns to make decisions by performing actions within an environment to maximize a mathematical reward signal.
How does it work?
The model is not told which actions to take. Instead, it must discover which actions yield the highest reward by trial and error. If it takes a good action, the reward signal increases. If it makes a mistake, it receives a penalty. Over millions of iterations, the model learns complex strategies to maximize its cumulative reward.
What is a simple example?
Training an AI to play chess using reinforcement learning involves the AI playing millions of games against itself. Initially, it moves pieces randomly. If a sequence of moves leads to a checkmate (a win), the system reinforces the neural pathways that led to those moves. If it loses, those pathways are penalized.
Why does it matter?
Reinforcement learning is the key technique used to train Reasoning Models and game-playing AIs (like AlphaGo). Furthermore, a specific variation called Reinforcement Learning from Human Feedback (RLHF) is used to fine-tune Large Language Models, ensuring they behave politely, follow instructions, and avoid generating harmful content.