Reinforcement Learning (RL) in Data Science has key features like learning from interactions, maximizing rewards, and dealing with sequential decision-making. It involves an agent interacting with an environment, taking actions based on a policy, receiving rewards, and learning to optimize its decision-making through trial and error.
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