Searched for: subject%3A%22Deep%255C+reinforcement%255C+learning%22
(41 - 60 of 95)
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Ferreira Lemos, André (author)Even though Deep Reinforcement Learning (DRL) techniques have proven their ability to solve highly complex control tasks, the opaqueness and inexplicability associated with these solutions many times stops them from being applied to real flight control applications. In this research, reward decomposition explanations are used to tackle this...master thesis 2022
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Teirlinck, Casper (author)Recent advancements in fault-tolerant flight control have involved model-free offline and online Reinforcement Learning algorithms in order to provide robust and adaptive control to autonomous systems. Inspired by recent work on Incremental Dual Heuristic Programming (IDHP) and Soft Actor-Critic (SAC), this research proposes a hybrid SAC-IDHP...master thesis 2022
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Rajesh, Nishant (author)Motion sickness is a common phenomenon, with close to two-thirds of the population experiencing it in their lifetime. With the advent of automated vehicles in the market, it is anticipated to become an even greater problem as the passengers face a lack of predictability of motion and loss of control over the vehicle. This could nullify the host...master thesis 2022
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Ni, Ying-Chuan (author)Adaptive Cruise Control (ACC) relieves human drivers’ tasks by taking over the control of the throttle and braking of the vehicles automatically. However, it has been demonstrated in many empirical studies that current production ACC systems fail to guarantee string stability. It is believed that if vehicles can take the longitudinal dynamics...master thesis 2022
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Lathourakis, Christos (author)An issue of utmost significance constitutes the maintenance of engineering systems exposed to corrosive environments, e.g. coastal and marine environments, highly acidic environments, etc. The most beneficial sequence of maintenance decisions, i.e. the one that corresponds to the minimum maintenance cost, can be sought as the solution to an...master thesis 2022
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Agrawal, Arpit (author)With the prospects of decentralized multi-agent systems becoming more prevalent in daily life, automated negotiation agents have made their place in these collaborative settings. They are an approach to promote communication between the agents in reaching solutions that are better for all involved.<br/><br/>Recent literature has shown great...bachelor thesis 2022
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Saaybi, Serge (author)Robotic agents can continuously provide feedback to people based on their behaviors. For instance, a robot swarm can remind a group of people to respect social distancing guidelines during a pandemic or discourage unwanted behavior such as littering. However, developing a swarm robot to operate in realistic situations is challenging: a robot...master thesis 2022
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Groot, D.J. (author), Ribeiro, M.J. (author), Ellerbroek, Joost (author), Hoekstra, J.M. (author)Current estimates show that the presence of unmanned aviation is likely to grow exponentially over the course of the next decades. Even with the more conservative estimates, these expected high traffic densities require a re-evaluation of the airspace structure to ensure safe and efficient operations. One structure that scored high on both the...conference paper 2022
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Lee, J. (author), Mitici, M.A. (author)The increasing availability of sensor monitoring data has stimulated the development of Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However, existing studies focus either on RUL prognostics only, or propose maintenance planning based on simple assumptions about degradation trends. We propose a framework to integrate...journal article 2022
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Bai, C. (author), Yan, Peng (author), Pan, W. (author), Guo, Jifeng (author)Multi-robot formation control has been intensively studied in recent years. In practical applications, the multi-robot system's ability to independently change the formation to avoid collision among the robots or with obstacles is critical. In this study, a multi-robot adaptive formation control framework based on deep reinforcement learning...journal article 2022
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Yang, Fan (author), Li, Xueyuan (author), Liu, Qi (author), Li, Z. (author), Gao, Xin (author)In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the...journal article 2022
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Zheng, Jingjing (author), Li, Kai (author), Mhaisen, N. (author), Ni, Wei (author), Tovar, Eduardo (author), Guizani, Mohsen (author)Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT). On the one hand, the learning accuracy of FL can be improved by selecting the IoT devices with large data sets for training, which gives rise to a higher energy...journal article 2022
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Andriotis, C. (author), Papakonstantinou, K.G. (author)Inspection and maintenance (I&M) optimization entails many sources of computational complexity, among others, due to high-dimensional decision and state variables in multi-component systems, long planning horizons, stochasticity of objectives and constraints, and inherent uncertainties in measurements and models. This paper studies how the...conference paper 2022
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Neustroev, G. (author), Andringa, S.P.E. (author), Verzijlbergh, R.A. (author), de Weerdt, M.M. (author)Wind farms suffer from so-called wake effects: when turbines are located in the wind shadows of other turbines, their power output is substantially reduced. These losses can be partially mitigated via actively changing the yaw from the individually optimal direction. Most existing wake control techniques have two major limitations: they use...conference paper 2022
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Arslan, Furkan (author), Aydoğan, Reyhan (author)Designing an effective and intelligent bidding strategy is one of the most compelling research challenges in automated negotiation, where software agents negotiate with each other to find a mutual agreement when there is a conflict of interests. Instead of designing a hand-crafted decision-making module, this work proposes a novel bidding...journal article 2022
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Bai, C. (author), Yan, Peng (author), Yu, Xiaoqiang (author), Guo, Jifeng (author)Unmanned and intelligent technologies are the future development trend in the business field. It is of great significance for the connotation analysis and application characterization of massive interactive data. Particularly, during major epidemics or disasters, how to provide business services safely and securely is crucial. Specifically,...journal article 2022
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Altamimi, Abdulelah (author), Lagoa, Constantino (author), Borges, José G. (author), McDill, Marc E. (author), Andriotis, C. (author), Papakonstantinou, K. G. (author)Forest management can be seen as a sequential decision-making problem to determine an optimal scheduling policy, e.g., harvest, thinning, or do-nothing, that can mitigate the risks of wildfire. Markov Decision Processes (MDPs) offer an efficient mathematical framework for optimizing forest management policies. However, computing optimal MDP...journal article 2022
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Mhaisen, N. (author), Allahham, Mhd Saria (author), Mohamed, Amr (author), Erbad, Aiman (author), Guizani, Mohsen (author)Service provisioning systems assign users to service providers according to allocation criteria that strike an optimal trade-off between users' Quality of Experience (QoE) and the operation cost endured by providers. These systems have been leveraging Smart Contracts (SCs) to add trust and transparency to their criteria. However, deploying...journal article 2022
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Ferreira de Brito, B.F. (author), Agarwal, Achin (author), Alonso-Mora, J. (author)Autonomous navigation in dense traffic scenarios remains challenging for autonomous vehicles (AVs) because the intentions of other drivers are not directly observable and AVs have to deal with a wide range of driving behaviors. To maneuver through dense traffic, AVs must be able to reason how their actions affect others (interaction model)...journal article 2022
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Páramo-Balsa, Paula (author), Gonzalez-Longatt, Francisco (author), Acosta, Martha N. (author), Rueda, José L. (author), Palensky, P. (author), Sanchez, Francisco (author), Roldan-Fernandez, Juan Manuel (author), Burgos-Payán, Manuel (author)The computational burden and the time required to train a deep reinforcement learning (DRL) can be appreciable, especially for the particular case of a DRL control used for frequency control of multi-electrical energy storage (MEESS). This paper presents an assessment of four training configurations of the actor and critic network to determine...conference paper 2022
Searched for: subject%3A%22Deep%255C+reinforcement%255C+learning%22
(41 - 60 of 95)