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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 ...
Differential reinforcement of low response rate (DRL) – Used to encourage low rates of responding. It is like an interval schedule, except that premature responses reset the time required between behavior. Differential reinforcement of high rate (DRH) – Used to increase high rates of responding. It is like an interval schedule, except that ...
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 .
Many responses preceding reinforcement may become correlated with the reinforcer, but the final response receives the greatest weight in memory. Specific models are provided for the three basic principles to articulate predicted response patterns in many different situations and under different schedules of reinforcement.
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)."
The behavioral momentum framework also has been used to account for the partial-reinforcement extinction effect (Nevin & Grace, 1999), to assess the persistence of drug-maintained behavior (Jimenez-Gomez & Shahan, 2007; Shahan & Burke, 2004), to increase task compliance (e.g., Belfiore, Lee, Scheeler & Klein, 2002), and to understand the ...
Edward Thorndike had a powerful impact on reinforcement theory and behavior analysis, providing the basic framework for empirical laws in behavior psychology with his law of effect. Through his contributions to the behavioral psychology field came his major impacts on education, where the law of effect has great influence in the classroom.
A central method was the [1] examination of functional relations between environment and behavior, as opposed to hypothetico-deductive learning theory [2] that had grown up in the comparative psychology of the 1920–1950 period. Skinner's approach was characterized by observation of measurable behavior which could be predicted and controlled.