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In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning .
Just as "reward" was commonly used to alter behavior long before "reinforcement" was studied experimentally, the Premack principle has long been informally understood and used in a wide variety of circumstances. An example is a mother who says, "You have to finish your vegetables (low frequency) before you can eat any ice cream (high frequency)."
Behavioral economics is the study of the psychological (e.g. cognitive, behavioral, affective, social) factors involved in the decisions of individuals or institutions, and how these decisions deviate from those implied by traditional economic theory. [1] [2] Behavioral economics is primarily concerned with the bounds of rationality of economic ...
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...
Knowledge of results is a term in the psychology of learning. [1] [2]: 619 A psychology dictionary defines it as feedback of information: "(a) to a subject about the correctness of [their] responses; (b) a student about success or failure in mastering material, or (c) a client in psychotherapy about progress".
It studies how economic behavior can shape our understanding of the brain, and how neuroscientific discoveries can guide models of economics. [ 1 ] It combines research from neuroscience , experimental and behavioral economics , and cognitive and social psychology.
Melioration is a form of matching where the subject is constantly shifting its behavior from the poorer reinforcement schedule to the richer reinforcement schedule, until it is spending most of its time at the richest variable interval schedule. By matching, the subject is equalizing the price of the reinforcer they are working for.
In the context of machine learning, the exploration–exploitation tradeoff is fundamental in reinforcement learning (RL), a type of machine learning that involves training agents to make decisions based on feedback from the environment. Crucially, this feedback may be incomplete or delayed. [4]