Reinforcement Learning (RL) is an area of machine learning where an agent learns to make decisions by performing certain actions and observing the rewards or feedback from those actions. It’s distinct from other types of machine learning because it focuses on how an agent should take actions in an environment to maximize some notion of cumulative reward. RL is widely used in various fields such as robotics, gaming, healthcare, finance, and more, for tasks that require a sequence of decisions.
- Agent and Environment: The RL process involves an agent that makes decisions and an environment in which the agent operates.
- Rewards: The agent learns to achieve a goal in an uncertain, potentially complex environment by trial and error. Positive rewards reinforce desired actions, while negative rewards discourage undesired actions.
- Applications: RL is used in self-driving cars (where the car learns to make decisions while driving), in playing games (like chess or Go), in robotics (for learning complex maneuvers), etc.
For example, in a gaming application, an RL agent learns to play and improve its game strategy by continually playing the game, making decisions, and improving based on the outcomes of these decisions.