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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 ...
For example, another person is providing the reinforcement. The Premack principle is a special case of reinforcement elaborated by David Premack, which states that a highly preferred activity can be used effectively as a reinforcer for a less-preferred activity. [14]: 123
Q-learning is a model-free reinforcement learning algorithm that teaches an agent to assign values to each action it might take, conditioned on the agent being in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.
The theory assumes that this pairing creates an association between the CS and the US through classical conditioning and, because of the aversive nature of the US, the CS comes to elicit a conditioned emotional reaction (CER) – "fear." b) Reinforcement of the operant response by fear-reduction.
The Premack principle may be violated if a situation or schedule of reinforcement provides much more of the high-probability behavior than of the low-probability behavior. Such observations led the team of Timberlake and Allison (1974) to propose the response deprivation hypothesis. [5]
The goals of learning are understanding and prediction. Learning falls into many categories, including supervised learning, unsupervised learning, online learning, and reinforcement learning. From the perspective of statistical learning theory, supervised learning is best understood. [4] Supervised learning involves learning from a training set ...
The third principle of MPR states that the coupling between a response and a reinforcer decreases with increased time between them (Killeen & Sitomer, 2003). Mathematical principles of reinforcement describe how incentives fuel behavior, how time constrains it, and how contingencies direct it.
Similarly to RLHF, reinforcement learning from AI feedback (RLAIF) relies on training a preference model, except that the feedback is automatically generated. [43] This is notably used in Anthropic 's constitutional AI , where the AI feedback is based on the conformance to the principles of a constitution.