Searched for: subject%3A%22Deep%255C+reinforcement%255C+learning%22
(61 - 80 of 95)
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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
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Groot, Jan (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...master thesis 2021
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Vilhjálmsson, Ingimundur (author)Natural disasters can destroy communication network components, potentially leading to severe losses in connectivity. During those devastating events, network connectivity is crucial for rescue teams as well as anyone in need of assistance. Therefore, swift network restoration following a disaster is vital. However, post-disaster network...master thesis 2021
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Smit, Jordi (author)Offline reinforcement learning, or learning from a fixed data set, is an attractive alternative to online reinforcement learning. Offline reinforcement learning promises to address the cost and safety implications of taking numerous random or bad actions online, which is a crucial aspect of traditional reinforcement learning that makes it...master thesis 2021
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Meral, Murat Kaan Meral (author)Automated asset trading is a crucial method used by financial entities such as investment firms or hedge funds. It allows them to allocate their capital in order to maximize their rate of returns. In scientific literature, there are multiple models suggested to solve this problem. However, these models either lack the complexity to understand...bachelor thesis 2021
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Brinkman, Sant (author)Mobile robots that operate in human environments require the ability to safely navigate among humans and other obstacles. Existing approaches use Deep Reinforcement Learning (DRL) to obtain safe robot behavior in such environments, but do not ensure collision avoidance or trajectory feasibility. This issue is solved by methods combining DRL with...master thesis 2021
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Çapanoğlu, Alp (author)One of the most challenging types of environments for a Deep Reinforcement Learning agent to learn in are those with sparse reward functions. There exist algorithms that are designed to perform well in settings with sparse rewards, but they are often applied to continuous state-action spaces, since economically relevant problems like robotic...bachelor thesis 2021
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Klaoudatos, Dimitrios (author)This MSc thesis presents the development of a viewpoint optimization framework to face the problem of detecting occluded fruits in autonomous harvesting. A Deep Reinforcement Learning (DRL) algorithm is developed in order to train a robotic manipulator to navigate to occlusion-free viewpoints of the tomato-target. Two Convolutional Neural...master thesis 2021
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Dally, Killian (author)Fault-tolerant flight control faces challenges as developing a model-based controller for each unexpected failure is unrealistic, and online learning methods can handle limited system complexity due to their low sample efficiency. In this research, a model-free coupled-dynamics flight controller for a jet aircraft able to withstand multiple...master thesis 2021
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van den Haak, Daniel (author)Lane change decision-making is an important challenge for automated vehicles, urging the need for high performance algorithms that are able to handle complex traffic situations. Deep reinforcement learning (DRL), a machine learning method based on artificial neural networks, has recently become a popular choice for modelling the lane change...master thesis 2021
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Albers, N. (author), Suau, M. (author), Oliehoek, F.A. (author)Deep Reinforcement Learning (RL) is a promising technique towards constructing intelligent agents, but it is not always easy to understand the learning process and the factors that impact it. To shed some light on this, we analyze the Latent State Representations (LSRs) that deep RL agents learn, and compare them to what such agents should...conference paper 2021
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Hribar, Jernej (author), Shinkuma, Ryoichi (author), Iosifidis, G. (author), Dusparic, Ivana (author)Millions of sensors, cameras, meters, and other edge devices are deployed in networks to collect and analyse data. In many cases, such devices are powered only by Energy Harvesting (EH) and have limited energy available to analyse acquired data. When edge infrastructure is available, a device has a choice: to perform analysis locally or offload...conference paper 2021
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He, Nan (author), Yang, S. (author), Li, Fan (author), Trajanovski, S. (author), Kuipers, F.A. (author), Fu, Xiaoming (author)The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different quality of service (QoS) requirements. Given the importance of...conference paper 2021
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Andriotis, C. (author), Papakonstantinou, K. G. (author)Determination of inspection and maintenance policies for minimizing long-term risks and costs in deteriorating engineering environments constitutes a complex optimization problem. Major computational challenges include the (i) curse of dimensionality, due to exponential scaling of state/action set cardinalities with the number of components; ...journal article 2021
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Zhang, Rongkai (author), Zhu, Jiang (author), Zha, Zhiyuan (author), Dauwels, J.H.G. (author), Wen, Bihan (author)State-of-the-art image denoisers exploit various types of deep neural networks via deterministic training. Alternatively, very recent works utilize deep reinforcement learning for restoring images with diverse or unknown corruptions. Though deep reinforcement learning can generate effective policy networks for operator selection or architecture...conference paper 2021
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Ferreira de Brito, B.F. (author), Everett, Michael (author), How, Jonathan Patrick (author), Alonso-Mora, J. (author)Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the environment conditions are continuously changing. Local trajectory optimization methods, such as model predictive control (MPC), can deal with those changes but require...journal article 2021
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Agarwal, Achin (author)The successful integration of autonomous vehicles (AVs) in human environments is highly dependent on their ability to navigate safely and timely through dense traffic conditions. Such conditions involve a diverse range of human behaviors, ranging from cooperative (willing to yield) to non-cooperative human drivers (unwilling to yield) that need...master thesis 2020
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Mandersloot, A.V. (author), Oliehoek, F.A. (author), Czechowski, A.T. (author)In this study, we investigate the effects of conditioning Independent Q-Learners (IQL) not solely on the individual action-observation history, but additionally on the sufficient plan-time statistic for Decentralized Partially Observable Markov Decision Processes. In doing so, we attempt to address a key shortcoming of IQL, namely that it is...conference paper 2020
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Kovács, B. (author)Deep reinforcement learning went through an unprecedented development in the last decade, resulting in agents defeating world champion human players in complex board games like go and chess. With few exceptions, deep reinforcement learning research focuses on fully observable environments, while there is slightly less research in the direction...master thesis 2020
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Mandersloot, A.V. (author)The Decentralized Partially Observable Markov Decision Process is a commonly used framework to formally model scenarios in which multiple agents must collaborate using local information. A key difficulty in a Dec-POMDP is that in order to coordinate successfully, an agent must decide on actions not only using its own information, but also by...master thesis 2020
Searched for: subject%3A%22Deep%255C+reinforcement%255C+learning%22
(61 - 80 of 95)