Reinforcement Learning (RL) is a subfield of machine learning focused on teaching agents to make sequential decisions in an environment to maximize cumulative rewards. Unlike supervised learning, where models are trained on labeled data, RL algorithms learn from interaction with an environment without any explicit guidance. This makes RL suitable for scenarios where an agent must learn to explore and optimize its behavior through trial and error.

RL involves an agent, an environment, and a feedback mechanism. The agent interacts with the environment by observing its current state, taking actions, and receiving rewards as feedback. It learns to associate specific actions with states to maximize the total expected reward.

Let's consider an example to understand this better. Imagine teaching a robot to navigate a maze. At the beginning, the robot will explore different paths, collect rewards if it finds the exit, and receive penalties for hitting walls. By continuously exploring and collecting feedback, the robot learns the optimal strategy to reach the goal efficiently.

Reinforcement Learning is a fascinating concept that allows machines to learn and adapt to complex environments. Stay tuned as we delve deeper into its components and algorithms in our upcoming posts!