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Reinforcement Learning is a type of Machine Learning where an agent learns to make decisions by taking actions in an environment to achieve a goal, receiving rewards or penalties for its actions, and adjusting its behavior accordingly. The agent's objective is to maximize the total reward over time, learning through trial and error which actions lead to positive outcomes and which do not. This approach differs from other Machine Learning techniques such as supervised and unsupervised learning, where labeled data or patterns in unlabeled data are used for training respectively. Reinforcement Learning is particularly well-suited for problems with delayed rewards, sequential decision making, and large state spaces, making it a valuable tool in areas like robotics, gaming, resource management, and autonomous systems. As a subfield of Artificial Intelligence and Artificial Neural Networks, Reinforcement Learning leverages the power of these technologies to process complex inputs, model dynamic environments, and optimize learning algorithms for efficient decision making. By building upon the foundations laid by its parent categories, Reinforcement Learning represents a significant advance in the development of intelligent systems capable of adapting and learning from interactions with their environment.