Searched for: subject%3A%22Multi%255C-agent%255C+reinforcement%255C+learning%22
(1 - 14 of 14)
document
Menor de Oñate, Adrian (author)
Exploring planetary bodies using robot swarms can potentially increase the value of the exploration missions; enabling the execution of novel measurements and explorations previously deemed impractical or unattainable. Despite its potential, the technology readiness level of planetary swarms is not very mature. This work uses multi-agent...
master thesis 2024
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Yeh, Jefferson (author)
Wind energy, generated by windfarms, is playing an increasingly critical role in meeting current and future energy demands. windfarms, however, face a challenge due to the inherent flaw of wake-induced power losses when turbines are located in close proximity. Wakes, characterized by regions of turbulence and lower wind speed, are created as air...
bachelor thesis 2023
document
van Selm, Jasper (author)
When multiple wind turbines are positioned close to one another, such as in a wind farm, wind turbines located downwind of other turbines are not 100% efficient due to wakes, negatively affecting the total power output of the wind farm. A way to mitigate the loss of power is to steer the wake away from the next turbine, which lowers the current...
bachelor thesis 2023
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Filimon, Mihai (author)
The close proximity of wind turbines to one another in a wind farm can lead to inefficiency in terms of power production due to wake effects. One technique to mitigate the losses is to veer from their individual optimal direction. As such, the wakes can be steered away from downstream turbines in order to increase the overall power output. Multi...
bachelor thesis 2023
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Plămădeală, Ion (author)
The wake effect which is turbulence behind a wind turbine created when it extracts energy negatively impacts the power output of the downstream turbines. Active Wake Control can mitigate this effect, by rotating some turbines away from the wind. Previous research applied single agent reinforcement learning to apply Active Wake Control, show- ing...
bachelor thesis 2023
document
van der Schaaf, Guus (author)
In wind farms wind turbines are often placed close to each other. Each turbine generates a turbulent wake field, this field negatively affects subsequent turbines. This can cost more than 12% efficiency. To decrease this loss we can steer the turbines away from the wind direction, this will decrease the individual turbine power output, but can...
bachelor thesis 2023
document
Serra Gomez, A. (author), Zhu, H. (author), Ferreira de Brito, B.F. (author), Böhmer, J.W. (author), Alonso-Mora, J. (author)
Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions to avoid collisions. However, the risk of collision between robots varies as they move and communication may not always be needed. This paper presents an efficient communication method that addresses the problem of “when...
journal article 2023
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Chen, Yangkun (author), Yu, Chenghui (author), Zhu, Hengman (author), Liu, Shuai (author), Zhang, Yibing (author), Suarez, Joseph (author), Zhao, Liang (author), He, J. (author), Chen, Jiaxin (author)
We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions. This competition targets robustness and generalization in multi-agent systems: participants train teams of agents to complete a multi-task objective against opponents not seen during training. We summarize the competition design...
journal article 2023
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Madi, Mohamed (author)
High level decision making in Autonomous Driving (AD) is a challenging task due to the presence of multiple actors and complex driving interactions. Multi-Agent Reinforcement Learning (MARL) has been proposed to learn multiple driving policies concurrently to solve AD tasks. In the literature, multi-agent algorithms have been shown to outperform...
master thesis 2022
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Castellini, Jacopo (author), Devlin, Sam (author), Oliehoek, F.A. (author), Savani, Rahul (author)
Policy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning. A key challenge, however, that is not addressed by many of these methods is multi-agent credit assignment: assessing an agent’s contribution to the overall performance, which is crucial for learning good policies. We...
journal article 2022
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Weijs, George (author)
Bus bunching is a problem that occurs in many high frequent bus systems. This can be averted by several countermeasures of which holding control is the most popular one in practice. Holding control strategies are often implemented using predefined rules. In this study, multi-agent reinforcement learning is selected to develop an effective...
master thesis 2021
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Castellini, Jacopo (author), Oliehoek, F.A. (author), Devlin, Sam (author), Savani, Rahul (author)
Policy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning. A key challenge, however, that is not addressed by many of these methods is multi-agent credit assignment: assessing an agent’s contribution to the overall performance, which is crucial for learning good policies. We propose...
conference paper 2021
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Niu, Y. (author), Schulte, F. (author), Negenborn, R.R. (author)
Human aspects in collaboration of humans and robots, as common in warehousing, are considered increasingly important objectives in operations management. This work aims to let robots learn about human discomfort in collaborative order picking of robotic mobile fulfillment systems. To this end, a multi-agent reinforcement (MARL) approach that...
journal article 2021
document
Niu, Y. (author), Schulte, F. (author)
Human aspects in collaboration of humans and robots, as common in warehousing, are considered increasingly important objectives in operations management. In this work, we let robots learn about human stress levels based on sensor data in collaborative order picking of robotic mobile fulfillment systems. To this end, we develop a multi-agent...
conference paper 2021
Searched for: subject%3A%22Multi%255C-agent%255C+reinforcement%255C+learning%22
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