Reinforcement Learning In Machine Learning Definition

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Reinforcement Learning In Machine Learning Definition

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Reinforcement learning is a training method in machine learning where an algorithm or agent completes a task through trial and error An agent must explore a controlled environment and learn from its actions the optimal way to achieve a certain goal Reinforcement learning (RL) is a machine learning (ML) technique that trains software to make decisions to achieve the most optimal results. It mimics the trial-and-error learning process that humans use to achieve their goals. Software actions that work towards your goal are reinforced, while actions that detract from the goal are ignored.

Types Of Machine Learning Supervised Unsupervised Reinforcement

Reinforcement Learning In Machine Learning DefinitionReinforcement learning is a machine learning training method based on rewarding desired behaviors and punishing undesired ones. In general, a reinforcement learning agent -- the entity being trained -- is able to perceive and interpret its environment, take actions and learn through trial and error. Reinforcement learning is one of three basic machine learning paradigms alongside supervised learning and unsupervised learning Reinforcement learning differs from supervised learning in not needing labelled input output pairs to be presented and in not needing sub optimal actions to be explicitly corrected

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