86 research outputs found
Ensemble Reinforcement Learning: A Survey
Reinforcement Learning (RL) has emerged as a highly effective technique for
addressing various scientific and applied problems. Despite its success,
certain complex tasks remain challenging to be addressed solely with a single
model and algorithm. In response, ensemble reinforcement learning (ERL), a
promising approach that combines the benefits of both RL and ensemble learning
(EL), has gained widespread popularity. ERL leverages multiple models or
training algorithms to comprehensively explore the problem space and possesses
strong generalization capabilities. In this study, we present a comprehensive
survey on ERL to provide readers with an overview of recent advances and
challenges in the field. First, we introduce the background and motivation for
ERL. Second, we analyze in detail the strategies that have been successfully
applied in ERL, including model averaging, model selection, and model
combination. Subsequently, we summarize the datasets and analyze algorithms
used in relevant studies. Finally, we outline several open questions and
discuss future research directions of ERL. By providing a guide for future
scientific research and engineering applications, this survey contributes to
the advancement of ERL.Comment: 42 page
Epidemic Spreading Characteristics and Immunity Measures Based on Complex Network with Contact Strength and Community Structure
Middle East Respiratory Syndrome (MERS), bursting in South Korea from May 2015 and mainly spreading within the hospitals at the beginning, has caused a large scale of public panic. Aiming at this kind of epidemic spreading swiftly by intimate contact within community structure, we first established a spreading model based on contact strength and SI model, and a weighted network with community structure based on BBV network model. Meanwhile, the sufficient conditions were deduced to ensure the optimal community division. Next, after the verification by the real data of MERS, it is found that the spreading rate is closely related to the average weight of network but not the number of communities. Then, as the further study shows, the final infection proportion declines with the decreases both in isolation delay and in average weight; however, this proportion can only be postponed rather than decreased with respect to sole average weight reduction without isolation. Finally, the opportunities to take action can be found to restrain the epidemic spreading to the most extent
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities
Evolutionary algorithms (EA), a class of stochastic search methods based on
the principles of natural evolution, have received widespread acclaim for their
exceptional performance in various real-world optimization problems. While
researchers worldwide have proposed a wide variety of EAs, certain limitations
remain, such as slow convergence speed and poor generalization capabilities.
Consequently, numerous scholars actively explore improvements to algorithmic
structures, operators, search patterns, etc., to enhance their optimization
performance. Reinforcement learning (RL) integrated as a component in the EA
framework has demonstrated superior performance in recent years. This paper
presents a comprehensive survey on integrating reinforcement learning into the
evolutionary algorithm, referred to as reinforcement learning-assisted
evolutionary algorithm (RL-EA). We begin with the conceptual outlines of
reinforcement learning and the evolutionary algorithm. We then provide a
taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the
RL-assisted strategy adopted by RL-EA, and its applications according to the
existing literature. The RL-assisted procedure is divided according to the
implemented functions including solution generation, learnable objective
function, algorithm/operator/sub-population selection, parameter adaptation,
and other strategies. Finally, we analyze potential directions for future
research. This survey serves as a rich resource for researchers interested in
RL-EA as it overviews the current state-of-the-art and highlights the
associated challenges. By leveraging this survey, readers can swiftly gain
insights into RL-EA to develop efficient algorithms, thereby fostering further
advancements in this emerging field.Comment: 26 pages, 16 figure
Fast Computing Betweenness Centrality with Virtual Nodes on Large Sparse Networks
Betweenness centrality is an essential index for analysis of complex networks. However, the calculation of betweenness centrality is quite time-consuming and the fastest known algorithm uses time and space for weighted networks, where and are the number of nodes and edges in the network, respectively. By inserting virtual nodes into the weighted edges and transforming the shortest path problem into a breadth-first search (BFS) problem, we propose an algorithm that can compute the betweenness centrality in time for integer-weighted networks, where is the average weight of edges and is the average degree in the network. Considerable time can be saved with the proposed algorithm when , indicating that it is suitable for lightly weighted large sparse networks. A similar concept of virtual node transformation can be used to calculate other shortest path based indices such as closeness centrality, graph centrality, stress centrality, and so on. Numerical simulations on various randomly generated networks reveal that it is feasible to use the proposed algorithm in large network analysis
The rise of Chinese nationalism since the 1990s and its origins.
The first part of this paper aims to show how the new Chinese nationalism has come into being. Compared to the 1980s, the Chinese have been more confident in their traditional culture, the Chinese patriotism has become stronger, the Chinese attitude toward the West has turn more diverse, and academic researchers or observations on Chinese nationalism was found to be heated.Master of Science (Strategic Studies
Critical threshold for average weights () on networks with specified network size () and average degree ().
<p>Critical threshold for average weights () on networks with specified network size () and average degree ().</p
Illustration of representing the weighted network (a) by an unweighted network with virtual nodes (b).
<p>Illustration of representing the weighted network (a) by an unweighted network with virtual nodes (b).</p
- …