Meaning
A type of machine learning where an agent learns to make decisions by receiving rewards or penalties.
Definition
Reinforcement learning involves training an AI agent to achieve a goal in an environment by maximizing cumulative rewards.
Unlike supervised learningSupervised Learning is a method where AI models learn from labeled data, allowing them to make accurate predictions on new inputs. More, where the correct output is known, reinforcement learning requires the agent to learn the best strategies through trial and error, often in complex and dynamic environments.
This approach is particularly effective for tasks where actions need to be sequential and outcomes are dependent on a series of decisions.
Example
Reinforcement learning is used in game AI, allowing characters to learn strategies to win against human players.
For instance, in chess-playing AI, the system learns from each game, improving its ability to anticipate moves and develop winning strategies based on previous experiences.
