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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 .
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 ...
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 ...
Such rules could also be the result of optimization, realized through use of AI methods (such as Q-learning and other reinforcement learning techniques). [ 3 ] As part of non-equilibrium economics , [ 4 ] the theoretical assumption of mathematical optimization by agents in equilibrium is replaced by the less restrictive postulate of agents with ...
Learning models are a way of explaining and predicting strategic decisions in behavioral game theory. More specifically, they aim to explain how player's choices may change when given the chance to learn about their opponents or the game. [7] There are three different types of learning models. The first is reinforcement learning.
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.
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]