Information about Test

  1. Reinforcement learning

    Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize

  2. 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. 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

  4. 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

  5. Reinforcement

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

  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. 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

  8. Artificial neural network

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

  9. 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. 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