Margaret Rouse is an award-winning technical writer and teacher known for her ability to explain complex technical subjects simply to a non-technical, business audience. Over…
Q-learning is a term for an algorithm structure representing model-free reinforcement learning. By evaluating policy and using stochastic modeling, Q-learning finds the best path forward in a Markov decision process.
The technical makeup of the Q-learning algorithm involves an agent, a set of states and a set of actions per state.
The Q function uses weights for various steps in conjunction with a discount factor in order to value rewards.
Although it may seem like a simple idea, Q-learning is of paramount importance in many types of reinforcement learning and deep learning models. One of the best examples is where deep Q-learning is used to help machine learning programs to learn game-play strategies in various types of video games, for example, in Atari games from the 1980s. Here a convolutional neural network takes samples of game-play in order to work up a stochastic model that will help the computer know how to play the game better over time.
Q-learning has abundant potential for helping to advance artificial intelligence and machine learning.
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Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.
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