{"iri":"https://folio.openlegalstandard.org/RCQ5aTqEDpPwcRvtzkx0T6e","label":"Reinforcement Learning","sub_class_of":["https://folio.openlegalstandard.org/RBHMad8oNmYXkYHOHZLCgqv"],"parent_class_of":[],"is_defined_by":null,"see_also":[],"comment":null,"deprecated":false,"preferred_label":"RL","alternative_labels":[],"translations":{},"hidden_label":null,"definition":"Reinforcement Learning (RL) is a subset of machine learning that enables an artificial system, or agent, to optimize its behavior in a given environment by learning from feedback signals, such as rewards or punishments. The goal is to maximize the cumulative reward received, which is determined by a reward function representing the system's objectives (e.g., answering legal questions, drafting legal documents).","examples":[],"notes":[],"history_note":null,"editorial_note":null,"in_scheme":null,"identifier":null,"description":null,"source":null,"country":null}