Information about Test

  1. Reinforcement learning

    aimlexchange.com/search/wiki/page/Reinforcement_learning

    reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning

  2. Deep reinforcement learning

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    Deep reinforcement learning (DRL) uses deep learning and reinforcement learning principles to create efficient algorithms applied on areas like robotics

  3. Q-learning

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    Q-learning is a model-free reinforcement learning algorithm to learn a policy telling an agent what action to take under what circumstances. It does not

  4. Reinforcement

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    In behavioral psychology, reinforcement is a consequence applied that will strengthen an organism's future behavior whenever that behavior is preceded

  5. Model-free (reinforcement learning)

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    In reinforcement learning (RL), a model-free algorithm (as opposed to a model-based one) is an algorithm which does not use the transition probability

  6. Machine learning

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    human user for labeling. Reinforcement learning algorithms are given feedback in the form of positive or negative reinforcement in a dynamic environment

  7. Unsupervised learning

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    three main categories of machine learning, along with supervised and reinforcement learning. Semi-supervised learning, a related variant, makes use of

  8. Neural architecture search

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    hyperparameter optimization and is a subfield of automated machine learning (AutoML). Reinforcement learning (RL) can underpin a NAS search strategy. Zoph et al. applied

  9. Artificial neural network

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    major learning paradigms are supervised learning, unsupervised learning and reinforcement learning. They each correspond to a particular learning task

  10. Temporal difference learning

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    Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate

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