Deep Reinforcement Learning (DRL) is a trending field of research, showing great promise in challenging problems such as playing Atari, solving Go and controlling robots. While DRL agents perform well in practice we are still lacking the tools to analayze their performance. In this work we present the Semi-Aggregated MDP (SAMDP) model. A model best suited to describe policies exhibiting both sp...

Deep Reinforcement Learning (DRL) is a trending field of research, showing great promise in many challenging problems such as playing Atari, solving Go and controlling robots. While DRL agents perform well in practice we are still missing the tools to analayze their performance and visualize the temporal abstractions that they learn. In this paper, we present a novel method that automatically d...

Equipping characters with diverse motor skills is the current bottleneck of physics-based character animation. We propose a Deep Reinforcement Learning (DRL) framework that enables to learn and explore from reference motions. The key insight use loose space-time constraints, termed spacetime bounds, limit search space in an early termination fashion. As we only rely on specify our learning more...

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INTRODUCTION The aim of this study was to establish institutional diagnostic reference levels (DRLs) by summarising doses collected across the five computed tomography (CT) system in our institution. METHODS CT dose data of 15940 patients were collected retrospectively from May 2015 to October 2015 in five institutional scanners. The mean, 75th percentile and 90th percentile of the dose sprea...

BACKGROUND Accuracy of rapid diagnostic tests for dengue infection has been repeatedly estimated by comparing those tests with reference assays. We hypothesized that those estimates might be inaccurate if the accuracy of the reference assays is not perfect. Here, we investigated this using statistical modeling. METHODS/PRINCIPAL FINDINGS Data from a cohort study of 549 patients suspected of d...

Lesion studies show that the hippocampus is critically involved in timing behavior, but so far there has been little analysis of how it might encode time. We recorded the activity of 266 CA1 neurons, 51 CA3 neurons, and 219 entorhinal neurons from rats performing on a differential reinforcement of low rates (DRL) 15 sec schedule in which reinforcement was contingent on responses that occurred a...