Métaheuristiques pour la résolution de problème de covoiturage régulier de grande taille et d'une extension

La dispersion spatiale de l'habitat et des activités de ces dernières décennies a fortement contribué à un allongement des distances et des temps de trajets domicile-travail. Cela a pour conséquence un accroissement de l'utilisation des voitures particulières, notamment au sein et aux abords des grandes agglomérations. Afin de réduire les impacts dus à l'augmentation du trafic routier, des services de covoiturage, où des usagers ayant la même destination se regroupent en équipage pour se déplacer, ont été mis en place partout dans le monde. Nous présentons ici nos travaux sur le problème de covoiturage régulier. Dans cette thèse, le problème de covoiturage régulier a été modélisé et plusieurs métaheuristiques de résolution ont été implémentées, testées et comparées. La thèse est organisée de la façon suivante: tout d'abord, nous commençons par présenter la définition et la description du problème ainsi que le modèle mathématique associé. Ensuite, plusieurs métaheuristiques pour résoudre le problème sont présentées. Ces approches sont au nombre de quatre: un algorithme de recherche locale à voisinage variable, un algorithme à base de colonies de fourmis, un algorithme génétique guidée et un système multi-agents génétiques auto-adaptatif. Des expériences ont été menées pour démontrer l'efficacité de nos approches. Nous continuons ensuite avec la présentation et la résolution d'une extension du problème de covoiturage occasionel comportant plusieurs destinations. Pour terminer, une plate-forme de test et d'analyse pour évaluer nos approches et une plate-forme de covoiturage sont présentées dans l'annexe.Nowadays, the increased human mobility combined with high use of private cars increases the load on environment and raises issues about quality of life. The extensive use of private cars lends to high levels of air pollution, parking problem, traffic congestion and low transfer velocity. In order to ease these shortcomings, the car pooling program, where sets of car owners having the same travel destination share their vehicles, has emerged all around the world. We present here our research on the long-term car pooling problem. In this thesis, the long-term car pooling problem is modeled and metaheuristics for solving the problem are investigated. The thesis is organized as follows. First, the definition and description of the problem as well as its mathematical model are introduced. Then, several metaheuristics to effectively and efficiently solve the problem are presented. These approaches include a Variable Neighborhood Search Algorithm, a Clustering Ant Colony Algorithm, a Guided Genetic Algorithm and a Multi-agent Self-adaptive Genetic Algorithm. Experiments have been conducted to demonstrate the effectiveness of these approaches on solving the long-term car pooling problem. Afterwards, we extend our research to a multi-destination daily car pooling problem, which is introduced in detail manner along with its resolution method. At last, an algorithm test and analysis platform for evaluating the algorithms and a car pooling platform are presented in the appendix.ARRAS-Bib.electronique (620419901) / SudocSudocFranceF

Deep learning for video game playing

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

Deep learning based approaches for imitation learning.

Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable

Review of Deep Learning Algorithms and Architectures

Deep learning (DL) is playing an increasingly important role in our lives. It has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. The painstakingly handcrafted feature extractors used in traditional learning, classification, and pattern recognition systems are not scalable for large-sized data sets. In many cases, depending on the problem complexity, DL can also overcome the limitations of earlier shallow networks that prevented efficient training and abstractions of hierarchical representations of multi-dimensional training data. Deep neural network (DNN) uses multiple (deep) layers of units with highly optimized algorithms and architectures. This paper reviews several optimization methods to improve the accuracy of the training and to reduce training time. We delve into the math behind training algorithms used in recent deep networks. We describe current shortcomings, enhancements, and implementations. The review also covers different types of deep architectures, such as deep convolution networks, deep residual networks, recurrent neural networks, reinforcement learning, variational autoencoders, and others.https://doi.org/10.1109/ACCESS.2019.291220

Controlling a cargo ship without human experience based on deep Q-network

Human experience is regarded as an indispensable part of artificial intelligence in the process of controlling or decision making for autonomous cargo ships. In this paper, a novel Deep Q-Network-based (DQN) approach is proposed, which performs satisfactorily in controlling a cargo ship automatically without any human experience. At the very beginning, we use the model of KRISO Very Large Crude Carrier (KVLCC2) to describe a cargo ship. To manipulate this ship has to conquer great inertia and relatively insufficient driving force. Subsequently, customary waterways, regulations, conventions are described with Artificial Potential Field and value-functions in DQN. Based on this, the artificial intelligence of planning and controlling a cargo ship can be obtained by undertaking sufficient training, which can control the ship directly, while avoiding collisions, keeping its position in the middle of the route as much as possible. In simulation experiments, it is demonstrated that such an approach performs better than manual works and other traditional methods in most conditions, which makes the proposed method a promising solution in improving the autonomy level of cargo ships

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture