Instrumental learning involves corticostriatal circuitry and the dopaminergic system. This system is typically modeled in the reinforcement learning (RL) framework by incrementally accumulating reward values of states and actions. However, human learning also implicates prefrontal cortical mechanisms involved in higher level cognitive functions. The interaction of these systems remains poorly u...

A dosimetry system based on Al2O3:C radioluminescence (RL), and RADPOS, a novel 4D dosimetry system using microMOSFETs, were used to measure total scatter factors, (S(c,p))(f(clin))(det), for the CyberKnife robotic radiosugery system. New Monte Carlo calculated correction factors are presented and applied for the RL detector response for the 5, 7.5 and 10 mm collimators in order to correct for ...

In this paper, we introduce the idea of generalized relative order (respectively generalized relative lower order) of entire functions of two complex variables. Hence, we study some growth properties of entire functions of two complex variables on the basis of the definition of generalized relative order and generalized relative lower order of entire functions of two complex variables.

Reinforcement learning (RL) has been successfully used to solve many continuous control tasks. Despite its impressive results however, fundamental questions regarding the sample complexity of RL on continuous problems remain open. We study the performance of RL in this setting by considering the behavior of the Least-Squares Temporal Difference (LSTD) estimator on the classic Linear Quadratic R...

The encoding of letter position is a key aspect in all recently proposed models of visual-word recognition. We analyzed the impact of lexical frequency on letter position assignment by examining the temporal dynamics of lexical activation induced by pseudowords extracted from words of different frequencies. For each word (e.g., BRIDGE), we created two pseudowords: A transposed-letter (TL: BRIGD...

This article deals with a novel branch of Separation of Concerns, called Multi-Advisor Reinforcement Learning (MAd-RL), where a single-agent RL problem is distributed to n learners, called advisors. Each advisor tries to solve the problem with a different focus. Their advice is then communicated to an aggregator, which is in control of the system. For the local training, three off-policy bootst...

The basal ganglia are widely believed to be involved in the learned selection of actions. Building on this idea, reinforcement learning (RL) theories of optimal control have had some success in explaining the responses of their key dopaminergic afferents. While these model-free RL theories offer a compelling account of a range of neurophysiological and behavioural data, they offer only an incom...