Reinforcement learningReinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.
Multiclass classificationIn machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). While many classification algorithms (notably multinomial logistic regression) naturally permit the use of more than two classes, some are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies.
Agency (philosophy)Agency is the capacity of an actor to act in a given environment. It is independent of the moral dimension, which is called moral agency. In sociology, an agent is an individual engaging with the social structure. Notably, though, the primacy of social structure vs. individual capacity with regard to persons' actions is debated within sociology. This debate concerns, at least partly, the level of reflexivity an agent may possess. Agency may either be classified as unconscious, involuntary behavior, or purposeful, goal directed activity (intentional action).
