Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Reinke, Chris"'
Autor:
Alameda-Pineda, Xavier, Addlesee, Angus, García, Daniel Hernández, Reinke, Chris, Arias, Soraya, Arrigoni, Federica, Auternaud, Alex, Blavette, Lauriane, Beyan, Cigdem, Camara, Luis Gomez, Cohen, Ohad, Conti, Alessandro, Dacunha, Sébastien, Dondrup, Christian, Ellinson, Yoav, Ferro, Francesco, Gannot, Sharon, Gras, Florian, Gunson, Nancie, Horaud, Radu, D'Incà, Moreno, Kimouche, Imad, Lemaignan, Séverin, Lemon, Oliver, Liotard, Cyril, Marchionni, Luca, Moradi, Mordehay, Pajdla, Tomas, Pino, Maribel, Polic, Michal, Py, Matthieu, Rado, Ariel, Ren, Bin, Ricci, Elisa, Rigaud, Anne-Sophie, Rota, Paolo, Romeo, Marta, Sebe, Nicu, Sieińska, Weronika, Tandeitnik, Pinchas, Tonini, Francesco, Turro, Nicolas, Wintz, Timothée, Yu, Yanchao
Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary. While several robotic platfo
Externí odkaz:
http://arxiv.org/abs/2404.07560
Deep Neural Networks (DNNs) became the standard tool for function approximation with most of the introduced architectures being developed for high-dimensional input data. However, many real-world problems have low-dimensional inputs for which standar
Externí odkaz:
http://arxiv.org/abs/2311.16148
With the increasing presence of robots in our every-day environments, improving their social skills is of utmost importance. Nonetheless, social robotics still faces many challenges. One bottleneck is that robotic behaviors need to be often adapted a
Externí odkaz:
http://arxiv.org/abs/2206.03211
A longstanding goal in reinforcement learning is to build intelligent agents that show fast learning and a flexible transfer of skills akin to humans and animals. This paper investigates the integration of two frameworks for tackling those goals: epi
Externí odkaz:
http://arxiv.org/abs/2111.03110
Autor:
Reinke, Chris, Alameda-Pineda, Xavier
Transfer in Reinforcement Learning aims to improve learning performance on target tasks using knowledge from experienced source tasks. Successor Representations (SR) and their extension Successor Features (SF) are prominent transfer mechanisms in dom
Externí odkaz:
http://arxiv.org/abs/2110.15701
Designing agent that can autonomously discover and learn a diversity of structures and skills in unknown changing environments is key for lifelong machine learning. A central challenge is how to learn incrementally representations in order to progres
Externí odkaz:
http://arxiv.org/abs/2005.06369
Autor:
Reinke, Chris
Reinforcement learning (RL) allows to solve complex tasks such as Go often with a stronger performance than humans. However, the learned behaviors are usually fixed to specific tasks and unable to adapt to different contexts. Here we consider the cas
Externí odkaz:
http://arxiv.org/abs/2004.08600
In many complex dynamical systems, artificial or natural, one can observe self-organization of patterns emerging from local rules. Cellular automata, like the Game of Life (GOL), have been widely used as abstract models enabling the study of various
Externí odkaz:
http://arxiv.org/abs/1908.06663
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Reinke, Chris, Alameda-Pineda, Xavier
source code available at https://gitlab.inria.fr/robotlearn/xi_learning; Transfer in Reinforcement Learning aims to improve learning performance on target tasks using knowledge from experienced source tasks. Successor features (SF) are a prominent tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::6304363112d3503172cc1b1d6082bbb3
https://hal.inria.fr/hal-03426870
https://hal.inria.fr/hal-03426870