Reinforcement learning (RL) differs from other types of learning, such as supervised and unsupervised learning, in its basic approach and paradigm. In contrast to supervised learning, where a model learns from labelled examples, and unsupervised learning, where the model attempts to learn patterns and structures in Unlike unsupervised learning, where the model tries to find patterns and structures in unlabelled data, RL is about training agents to make sequential decisions in an environment by interacting with it and receiving feedback in the form of rewards or punishments. The RL agent explores the environment through trial and error, learning from the consequences of its actions and trying to maximise a cumulative reward signal over time, rather than having explicitly correct answers or predefined structures. This trial-and-error nature of RL allows it to cope with dynamic, complex and uncertain environments, making it suitable for tasks such as games, robotics and autonomous systems.