Search results for "Reinforcement learning"
showing 5 items of 95 documents
Editorial: On the Nature and Scope of Habits and Model-Free Control
2021
Reinforcement learning in a multi-agent framework for pedestrian simulation
2014
El objetivo de la tesis consiste en la utilización de Aprendizaje por refuerzo (Reinforcement Learning) para generar simulaciones plausibles de peatones en diferentes entornos. Metodología Se ha desarrollado un marco de trabajo multi-agente donde cada agente virtual que aprende un comportamiento de navegación por interacción con el mundo virtual en el que se encuentra junto con el resto de agentes. El mundo virtual es simulado con un motor físico (ODE) que está calibrado con parámetros de peatones humanos extraídos de la bibliografía de la materia. El marco de trabajo es flexible y permite utilizar diferentes algoritmos de aprendizaje (en concreto Q-Learning y Sarsa(lambda) en combinación c…
Termodinamica e Funzioni Mentali Complesse
2010
Computational Rationality as a Theory of Interaction
2022
Funding Information: This work was funded by the Finnish Center for AI and Academy of Finland (“BAD” and “Human Automata”). We thank our reviewers, Xiuli Chen, Joerg Mueller, Christian Guckelsberger, Sebastiaan de Peuter, Samuel Kaski, Pierre-Alexandre Murena, Antti Keuru-lainen, Suyog Chandramouli, and Roderick Murray-Smith for their comments. Publisher Copyright: © 2022 ACM. How do people interact with computers? This fundamental question was asked by Card, Moran, and Newell in 1983 with a proposition to frame it as a question about human cognition - in other words, as a matter of how information is processed in the mind. Recently, the question has been reframed as one of adaptation: how …
Trajectory Design and Resource Allocation for Multi-UAV Networks : Deep Reinforcement Learning Approaches
2023
The future mobile communication system is expected to provide ubiquitous connectivity and unprecedented services over billions of devices. The unmanned aerial vehicle (UAV), which is prominent in its flexibility and low cost, emerges as a significant network entity to realize such ambitious targets. In this work, novel machine learning-based trajectory design and resource allocation schemes are presented for a multi-UAV communications system. In the considered system, the UAVs act as aerial Base Stations (BSs) and provide ubiquitous coverage. In particular, with the objective to maximize the system utility over all served users, a joint user association, power allocation and trajectory desi…