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Decentralized reinforcement learning applied to mobile robots
(Springer, 2017)
In this paper, decentralized reinforcement learning is applied to a control problem with a multidimensional action space. We propose a decentralized reinforcement learning architecture for a mobile robot, where the individual ...
Decentralized reinforcement learning of robot behaviors
(Elsevier, 2018)
A multi-agent methodology is proposed for Decentralized Reinforcement Learning (DRL) of individual behaviors in problems where multi-dimensional action spaces are involved. When using this methodology, sub-tasks are learned ...
Toward real-time decentralized reinforcement learning using finite support basis functions
(Springer Verlag, 2017)
This paper addresses the design and implementation of complex Reinforcement Learning (RL) behaviors where multi-dimensional action spaces are involved, as well as the need to execute the behaviors in real-time using robotic ...
Accelerating decentralized reinforcement learning of complex individual behaviors
(Elsevier, 2019)
Many Reinforcement Learning (RL) real-world applications have multi-dimensional action spaces which suffer from the combinatorial explosion of complexity. Then, it may turn infeasible to implement Centralized RL (CRL) ...
GROWS - Improving decentralized resource allocation in wireless networks through graph neural networks
(ACM, 2022)
Wireless networks have progressed exponentially over the last decade, and modern wireless networking is today a complex to manage tangle, serving an ever-growing number of end-devices through a plethora of technologies. ...
Energy-Efficiency Oriented Traffic Offloading in Wireless Networks: A Brief Survey and a Learning Approach for Heterogeneous Cellular Networks
(IEEE, 2015)
This paper first provides a brief survey on existing traffic offloading techniques in wireless networks. Particularly as a case study, we put forward an online reinforcement learning framework for the problem of traffic ...
Data-driven control of multi-tank water systems : centralized and decentralized approaches with reinforcement learning
(UniandesMaestría en Ingeniería Electrónica y de ComputadoresFacultad de IngenieríaDepartamento de Ingeniería Eléctrica y Electrónica, 2019)
"Este trabajo investiga el control basado en datos de sistemas de tanques de agua con dinámica acoplada no lineal. Dichas dinámicas usualmente dificultan el diseño analítico de los controladores basados en modelos y requieren ...
Multi-Agent based decentralized reinforcement learning of individual behaviors
(Universidad de Chile, 2018)
El paradigma del aprendizaje reforzado (RL \footnote{Por sus siglas en inglés: Reinforcement Learning}) está siendo recurrentemente utilizado para aprender tareas complejas en
contextos aplicativos como la robótica. No ...
Portfolio optimization in cryptocurrencies: a comparison of deep reinforcement learning and traditional approaches
(Universidad de los AndesMaestría Internacional en FinanzasFacultad de Administración, 2022-12-16)
Cryptocurrencies have become appealing investment options in recent years because of their high potential returns. This asset class emerged as a unique investment opportunity with distinguishing characteristics such as ...
Diseño de una estrategia para la planeación de rutas de navegación autónoma de un robot móvil en entornos interiores usando un algoritmo de aprendizaje automático
(Medellín - Minas - Maestría en Ingeniería - Ingeniería de SistemasUniversidad Nacional de Colombia - Sede Medellín, 2020-10-09)
The problem of autonomous robot navigation in internal environments must overcome various difficulties such as the dimensionality of the data, the computational cost and the possible presence of mobile objects. This thesis ...