Reinforcement learning

Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards. It involves key concepts such as agents, environments, states, actions, rewards, policies, value functions, and algorithms like Q-Learning, which help the agent improve its decision-making over time. RL is particularly beneficial for applications requiring adaptive decision-making, such as robotics, game playing, and autonomous systems.