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In behavioral psychology, reinforcement refers to consequences that increase the likelihood of an organism's future behavior, typically in the presence of a particular antecedent stimulus. [1] For example, a rat can be trained to push a lever to receive food whenever a light is turned on. In this example, the light is the antecedent stimulus ...
Reinforcement learning ( RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and ...
The reinforcement is usually, though not necessarily, steel bars ( rebar) and is usually embedded passively in the concrete before the concrete sets. However, post-tensioning is also employed as a technique to reinforce the concrete. In terms of volume used annually, it is one of the most common engineering materials.
Various techniques exist to train policies to solve tasks with deep reinforcement learning algorithms, each having their own benefits. At the highest level, there is a distinction between model-based and model-free reinforcement learning, which refers to whether the algorithm attempts to learn a forward model of the environment dynamics.
Reinforcement is a process of speciation where natural selection increases the reproductive isolation (further divided to pre-zygotic isolation and post-zygotic isolation) between two populations of species. This occurs as a result of selection acting against the production of hybrid individuals of low fitness.
Operant conditioning. Operant conditioning, also called instrumental conditioning, is a learning process where voluntary behaviors are modified by association with the addition (or removal) of reward or aversive stimuli. The frequency or duration of the behavior may increase through reinforcement or decrease through punishment or extinction.
Q-learning is a model-free reinforcement learning algorithm to learn the value of an action 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. [1]
Reinforcement sensitivity theory (RST) proposes three brain-behavioral systems that underlie individual differences in sensitivity to reward, punishment, and motivation. While not originally defined as a theory of personality , the RST has been used to study and predict anxiety , impulsivity , and extraversion . [1]