… that this flexibility emerges through a reinforcement learning process, in which reward …
error signal. Consistent with predictions of a computational reinforcement learning model, we …

… signal may primarily reflect inputs (and local computation), it is possible that with human fMRI
such a full prediction error signal … , if conformity is based on reinforcement learning, (1) a …

… do learn to favor choice of the optimal action and those who do not. Using models of reinforcement
learning we … in the degree to which reinforcement learning signals in the striatum are …

… enhance reinforcement learning signals in the striatum and sharpen representations of
associative values in prefrontal cortex that are used to guide reinforcement-based decisions. …

… characterize their learning, and examined whether a teacher's ACC signals when … signals
PEs vicariously for others' erroneous predictions, when monitoring and instructing their learning…

… In the current study, we used a reinforcement learning model to investigate neurodevelopmental
changes in the representation and processing of learning signals. Sixty-seven healthy …

… Reinforcement learning (RL), which is an artificial intelligence approach, has been adopted
in traffic signal … makers (eg, traffic signal controllers) to observe, learn, and select the optimal …

… the reinforcement learning framework which constitutes the foundation of all the methods
presented in this paper. We then provide background on conventional RLbased traffic signal …

Improvement of traffic signal control (TSC) efficiency has been found to lead to improved
urban transportation and enhanced quality of life. Recently, the use of reinforcement learning (…

… is the self-adaptive signal controller that … signal control over the group of the signals. This
paper tries to realize this feature by letting each signal learn through Reinforcement Learning [5]…