Reinforcement Learning (RL) is a powerful type of machine learning where an agent learns to make decisions by interacting with an environment. Inspired by behavioral psychology, RL uses rewards and penalties to guide learning—much like training a dog to sit with treats.

In RL, the agent observes the state of the environment, performs an action, and receives a reward. Based on the result, it adjusts its strategy to maximize long-term reward. This process is governed by a policy, which maps states to actions.

Key concepts include:

• Environment: The world the agent interacts with (e.g., a game, a robot’s surroundings).
• State: The current situation the agent observes.
• Action: The choices the agent can make.
• Reward: Feedback given after an action.
• Q-Function / Value Function: Measures the expected future reward from a given state-action pair.

Popular RL algorithms include:

• Q-learning
• SARSA
• Deep Q-Networks (DQN)
• Policy Gradient Methods (e.g., REINFORCE)

One of the most famous applications of RL is in games—AlphaGo, Dota 2 bots, and OpenAI’s Gym agents all use reinforcement learning to outperform humans. It’s also used in robotics, recommendation systems, and self-driving cars.

Libraries like OpenAI Gym and Stable-Baselines make it easier to experiment with RL. While more complex than supervised learning, RL offers an exciting path to building truly autonomous systems that learn from interaction, not just data.