Information about Test

  1. 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

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

  3. Q-learning

    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

    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)

    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

    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

    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

    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

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

  10. Temporal difference learning

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