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Reinforcement learning focuses on learning to make decisions based on rewards from "summary" of Machine Learning by Stephen Marsland
Reinforcement learning is a type of machine learning that is centered around the idea of making decisions in order to receive rewards. The key concept here is that an agent, such as a robot or a computer program, is trained to take actions in an environment in order to maximize some notion of cumulative reward. This is in contrast to other forms of machine learning where the focus is on training a model on a labeled dataset to make predictions. In reinforcement learning, the agent learns through trial and error, receiving feedback in the form of rewards or punishments based on its actions. The goal is for the agent to learn a policy, which is a mapping from states to actions, that maximizes the expected cumulative reward over time. This involves exploring different actions in different states, learning which actions lead to the highest r...
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