Reinforcement learning


Topic | v1 | created by janarez |
Description

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. 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 be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).


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uses Q-learning

Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a part...

subtopic of Deep learning

Deep learning (also known as deep structured learning) is part of a broader family of machine learnin...


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