Reinforcement Learning (RL) is one of the most exciting and dynamic areas of machine learning, where an agent learns to make decisions by interacting with an environment and receiving feedback through rewards or penalties. Unlike supervised or unsupervised learning, where the model is trained on labeled data or patterns, RL systems are designed to improve their performance over time through trial and error. This process of learning through consequences is inspired by how humans and animals learn through experiences.
Exploring Reinforcement Learning: How Machines Learn Through Trial and Error
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