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 in order to create efficient algorithms that can be applied

  3. 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)

    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

    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

    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

    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

    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

    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

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