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

    aimlexchange.com/search/wiki/page/Deep_reinforcement_learning

    Deep reinforcement learning (DRL) uses deep learning and reinforcement learning principles in order to create efficient algorithms that can be applied

  3. Q-learning

    aimlexchange.com/search/wiki/page/Q-learning

    Q-learning is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under

  4. Model-free (reinforcement learning)

    aimlexchange.com/search/wiki/page/Model-free_%28reinforcement_learning%29

    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

  5. Machine learning

    aimlexchange.com/search/wiki/page/Machine_learning

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

  6. Neural architecture search

    aimlexchange.com/search/wiki/page/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

  7. Reinforcement

    aimlexchange.com/search/wiki/page/Reinforcement

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

  8. End-to-end reinforcement learning

    aimlexchange.com/search/wiki/page/End-to-end_reinforcement_learning

    In end-to-end reinforcement learning, the end-to-end process, in other words, the entire process from sensors to motors in a robot or agent involves a

  9. Temporal difference learning

    aimlexchange.com/search/wiki/page/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

  10. Multi-objective reinforcement learning

    aimlexchange.com/search/wiki/page/Multi-objective_reinforcement_learning

    Multi-objective reinforcement learning (MORL) is a form of reinforcement learning concerned with conflicting alternatives. It is distinct from multi-objective

Contents