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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 .
An experiment conducted by Solomon and Wynne [7] in 1953 shows the properties of negative reinforcement. The subjects, dogs, were put in a shuttle box (a chamber containing two rectangular compartments divided by a barrier a few inches high). The dogs had the ability to move freely between compartments by going over the barrier.
The experimental analysis of behavior is a science that studies the behavior of individuals across a variety of species. A key early scientist was B. F. Skinner who discovered operant behavior, reinforcers, secondary reinforcers, contingencies of reinforcement, stimulus control, shaping, intermittent schedules, discrimination, and generalization.
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 ...
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]
The first portion of an ICSS experiment involves training subjects to respond for stimulation using a fixed-ratio 1 (FR-1) reinforcement schedule (1 response = 1 reward). In experiments involving rats, subjects are trained to press a lever for stimulation, and the rate of lever-pressing is typically the dependent variable. [1]
Humanistic intelligence, which is intelligence that arises by having the human in the feedback loop of the computational process [9] Reinforcement learning from human feedback; MIM-104 Patriot - Examples of a human-on-the-loop lethal autonomous weapon system posing a threat to friendly forces.
Skinner described operant conditioning as strengthening behaviour through reinforcement. Reinforcement can consist of positive reinforcement, in which a desirable stimulus is added; negative reinforcement, in which an undesirable stimulus is taken away; positive punishment, in which an undesirable stimulus is added; and negative punishment, in which a desirable stimulus is taken away. [7]