Search results for " Reinforcement"
showing 10 items of 51 documents
Can Interpretable Reinforcement Learning Manage Prosperity Your Way?
2022
Personalisation of products and services is fast becoming the driver of success in banking and commerce. Machine learning holds the promise of gaining a deeper understanding of and tailoring to customers’ needs and preferences. Whereas traditional solutions to financial decision problems frequently rely on model assumptions, reinforcement learning is able to exploit large amounts of data to improve customer modelling and decision-making in complex financial environments with fewer assumptions. Model explainability and interpretability present challenges from a regulatory perspective which demands transparency for acceptance; they also offer the opportunity for improved insight into and unde…
Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop
2018
Active inference is an ambitious theory that treats perception, inference and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explanations for many cognitive phenomena, including consciousness. In active inference, action selection is driven by an objective function that evaluates possible future actions with respect to current, inferred beliefs about the world. Active inference at its core is independent from extrinsic rewards, resulting in a high level of robustness across e.g.\ different environments or agent morphologies. In the literature, paradigms that share this independence have been summarised under the notion of in…
On the use of Deep Reinforcement Learning for Visual Tracking: a Survey
2021
This paper aims at highlighting cutting-edge research results in the field of visual tracking by deep reinforcement learning. Deep reinforcement learning (DRL) is an emerging area combining recent progress in deep and reinforcement learning. It is showing interesting results in the computer vision field and, recently, it has been applied to the visual tracking problem yielding to the rapid development of novel tracking strategies. After providing an introduction to reinforcement learning, this paper compares recent visual tracking approaches based on deep reinforcement learning. Analysis of the state-of-the-art suggests that reinforcement learning allows modeling varying parts of the tracki…
Development of a Simulator for Prototyping Reinforcement Learning-Based Autonomous Cars
2022
Autonomous driving is a research field that has received attention in recent years, with increasing applications of reinforcement learning (RL) algorithms. It is impractical to train an autonomous vehicle thoroughly in the physical space, i.e., the so-called ’real world’; therefore, simulators are used in almost all training of autonomous driving algorithms. There are numerous autonomous driving simulators, very few of which are specifically targeted at RL. RL-based cars are challenging due to the variety of reward functions available. There is a lack of simulators addressing many central RL research tasks within autonomous driving, such as scene understanding, localization and mapping, pla…
The Mediating Role of Self-Efficacy in the Relationship between Approach Motivational System and Sports Success among Elite Speed Skating Athletes an…
2022
Background: While the association between self-efficacy and sports success has been well established in previous studies, little is known regarding whether the basic approach motivation system contributes to this relationship in athletes. The study examines associations between self-reported temperamental approach disposition, self-efficacy, and predispositions to sports success in athletes. Methods: A cross-sectional study was performed between August 3 and 30 November 2020. The participants were 156 athletes, aged 16–34 years (M = 21.57, SD = 3.58, 41.67% women), in two groups: 54 elite athletes in speed skating (EASS) and 102 physical education students (PES). The online survey consisted…
Acetaldehyde Oral Self-Administration: Evidence from the Operant-Conflict Paradigm
2011
Background: Acetaldehyde (ACD), ethanol's first metabolite, has been reported to interact with the dopaminergic reward system, and with the neural circuits involved in stress response. Rats self-administer ACD directly into cerebral ventricles, and multiple intracerebroventricular infusions of ACD produce conditioned place preference. Self-administration has been largely employed to assess the reinforcing and addictive properties of most drugs of abuse. In particular, operant conditioning is a valid model to investigate drug-seeking and drug-taking behavior in rats. Methods: This study was aimed at the evaluation of (i) the motivational properties of oral ACD in the induction and maintenanc…
Transgraft sac Embolization Combined with Graft Reinforcement for Refractory Mixed-Type Endoleak.
2018
International audience; An 80-year-old female underwent EVAR 4 years ago. She presented type II endoleak with sac expansion from 68 to 80 mm during 3-year follow-up after EVAR. Although she underwent translumbar percutaneous sac embolization, the AAA sac continued to enlarge, suggesting mixed-type endoleak including type I, II, and III. Transgraft direct sac angiography revealed endoleak cavity without demonstrable feeding vessel. Transgraft sac embolization using n-butyl cyanoacrylate and graft reinforcement was performed concurrently, without complications. The graft reinforcement consisted of graft extension for eliminating occult type I endoleak, and relining for eliminating occult type…
Designing a multi-layer edge-computing platform for energy-efficient and delay-aware offloading in vehicular networks
2021
Abstract Vehicular networks are expected to support many time-critical services requiring huge amounts of computation resources with very low delay. However, such requirements may not be fully met by vehicle on-board devices due to their limited processing and storage capabilities. The solution provided by 5G is the application of the Multi-Access Edge Computing (MEC) paradigm, which represents a low-latency alternative to remote clouds. Accordingly, we envision a multi-layer job-offloading scheme based on three levels, i.e., the Vehicular Domain, the MEC Domain and Backhaul Network Domain. In such a view, jobs can be offloaded from the Vehicular Domain to the MEC Domain, and even further o…
Brick Masonry Columns Externally Wrapped with Steel Wires under Concentric and Eccentric Loads
2017
This paper discusses an experimental investigation of clay brick columns that are externally strengthened by steel wire collars wrapping the horizontal mortar joints. The study aims to prove the effectiveness of the proposed strengthening technique and detect the efficiency of different numbers of steel collars. Additionally, the effect of eccentric loading is investigated. This paper proves that an analytical expression available in the previous literature can be modified to provide the strength of the equivalent homogeneous cross section in simple compression. To this aim, the biaxial strength domain of the bricks is modified to account for the lateral pressure exerted by the steel collar…
Preparation and Characterization of Composites Materials with Rubber Matrix and with Polyvinyl Chloride Addition (PVC)
2020
An important problem that arises at present refers to the increase in performances in the exploitation of the conveyor belts. Additionally, it is pursued to use some materials, which can be obtained by recycling rubber and PVC waste, in their structure. Thus, the research aimed at creating conveyor belts using materials obtained from the recycling of rubber and PVC waste. Under these conditions, conveyor belts were made that had in their structure two types of rubber and PVC, which was obtained by adding in certain proportions of reclaimed rubber and powder obtained from grinding rubber waste. In order to study the effect of adding PVC on properties, four types of conveyor belts were made, …