Reinforcement Learning
Definition
Reinforcement learning is a type of machine learning where an agent learns to make a sequence of decisions by trying to maximize a reward signal.
This learning process involves an agent interacting with an environment. The agent takes actions within this environment, and for each action, it receives feedback in the form of a reward or a penalty. Over time, the agent learns which actions lead to the most favorable outcomes by associating actions with the rewards they generate. This trial-and-error method allows the agent to develop a strategy, or policy, that guides its decision-making to achieve a specific goal.
For instance, a reinforcement learning agent might learn to play a video game by receiving points for successful moves and losing points for mistakes.
Reinforcement learning is a fundamental concept in the field of artificial intelligence and is commonly used in robotics, game playing, autonomous systems, and optimization problems.