Reinforcement Learning

What Is Reinforcement Learning?

Reinforcement learning is fundamentally different from supervised or unsupervised learning. Instead of learning from labeled examples or finding patterns in data, an agent learns by interacting with an environment. The agent takes actions, observes results, receives rewards (or penalties), and adjusts its strategy to maximize total rewards. This mirrors how humans and animals learn.

Reinforcement learning is powerful for sequential decision-making tasks where the outcome of today's action affects future states. It's used in game-playing AI (AlphaGo defeated world champions), robotics (teaching robots to walk or manipulate objects), autonomous vehicles, and recommendation systems. The learning process can be slow, requiring millions of interactions to master complex tasks.

Reinforcement learning involves balancing exploration (trying new strategies) and exploitation (using known good strategies). The agent must discover which actions lead to rewards while avoiding getting stuck in local optima. Modern techniques use neural networks to approximate the value of actions (deep reinforcement learning).