What is the definition of reinforcement learning? How to measure it?

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Reinforcement learning (RL) is a type of machine learning focused on how an agent should take actions in an environment to maximize cumulative rewards through trial and error. Unlike supervised learning, it does not require labeled input/output pairs; instead, it learns from the consequences of its actions, balancing exploration and exploitation to achieve optimal outcomes.

Measuring reinforcement learning involves evaluating the agent's performance based on its ability to maximize rewards over time. Metrics such as cumulative reward, learning rate, and success rate in achieving specific tasks are commonly used to assess the effectiveness of RL algorithms.