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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 ...
In behavioral psychology, reinforcement refers to ... on reinforcement is usually dated from the work of ... are fundamental to learning theory and ...
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)."
Reinforcement learning is a subset of machine learning. It enables an agent to learn through the consequences of actions in a specific environment. Reinforcement learning is a behavioral learning ...
Operant conditioning originated with Edward Thorndike, whose law of effect theorised that behaviors arise as a result of consequences as satisfying or discomforting. In the 20th century, operant conditioning was studied by behavioral psychologists, who believed that much of mind and behaviour is explained through environmental conditioning.
The reward system (the mesocorticolimbic circuit) is a group of neural structures responsible for incentive salience (i.e., "wanting"; desire or craving for a reward and motivation), associative learning (primarily positive reinforcement and classical conditioning), and positively-valenced emotions, particularly ones involving pleasure as a core component (e.g., joy, euphoria and ecstasy).
The reinforcement schedule can also be manipulated to determine how motivated an animal is to receive stimulation, reflected by how hard they are willing to work to earn it. This can be done by increasing the number of responses required to receive a reward (FR-2, FR-3, FR-4, etc.) or by implementing a progressive-ratio schedule, where the ...
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 .