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Machine learning’s reinforcement learning segment focuses on guiding software agents to make decisions in an environment to optimize cumulative rewards. When an algorithm demonstrates improved accuracy in its outputs or predictions over time, it is described as having learned the task. While every algorithm boasts specific strengths and weaknesses, no single one is universally applicable to all problems. Learning theorists examine not only performance limits but also the time complexity and practicality associated with learning.
