Reinforcement learning is a type of machine learning algorithm that involves an agent learning to make decisions in an environment based on feedback in the form of rewards or punishments. The goal of the agent is to maximize its cumulative reward over time, by taking actions that lead to positive outcomes and avoiding actions that lead to negative outcomes.
In reinforcement learning, the agent interacts with an environment by taking actions and receiving feedback in the form of rewards or punishments. The agent learns to associate its actions with the expected rewards, and uses this information to make better decisions in the future.
Reinforcement learning is commonly used in areas such as robotics, game playing, and control systems. It has also been used in various other applications such as autonomous vehicles, recommendation systems, and healthcare.
Examples of reinforcement learning algorithms include Q-learning, SARSA, and Actor-Critic. These algorithms use various techniques to balance the trade-off between exploration and exploitation, and to learn optimal policies that maximize the expected rewards over time.