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pro vyhledávání: '"Jacq, Alexis"'
Autor:
Jacq, Alexis, Orsini, Manu, Dulac-Arnold, Gabriel, Pietquin, Olivier, Geist, Matthieu, Bachem, Olivier
Recent advances in ML suggest that the quantity of data available to a model is one of the primary bottlenecks to high performance. Although for language-based tasks there exist almost unlimited amounts of reasonably coherent data to train from, this
Externí odkaz:
http://arxiv.org/abs/2211.03521
Publikováno v:
Autonomous Agents and Multi-Agent Systems (2022)
Traditionally, Reinforcement Learning (RL) aims at deciding how to act optimally for an artificial agent. We argue that deciding when to act is equally important. As humans, we drift from default, instinctive or memorized behaviors to focused, though
Externí odkaz:
http://arxiv.org/abs/2203.08542
Autor:
Hoffman, Matthew W., Shahriari, Bobak, Aslanides, John, Barth-Maron, Gabriel, Momchev, Nikola, Sinopalnikov, Danila, Stańczyk, Piotr, Ramos, Sabela, Raichuk, Anton, Vincent, Damien, Hussenot, Léonard, Dadashi, Robert, Dulac-Arnold, Gabriel, Orsini, Manu, Jacq, Alexis, Ferret, Johan, Vieillard, Nino, Ghasemipour, Seyed Kamyar Seyed, Girgin, Sertan, Pietquin, Olivier, Behbahani, Feryal, Norman, Tamara, Abdolmaleki, Abbas, Cassirer, Albin, Yang, Fan, Baumli, Kate, Henderson, Sarah, Friesen, Abe, Haroun, Ruba, Novikov, Alex, Colmenarejo, Sergio Gómez, Cabi, Serkan, Gulcehre, Caglar, Paine, Tom Le, Srinivasan, Srivatsan, Cowie, Andrew, Wang, Ziyu, Piot, Bilal, de Freitas, Nando
Deep reinforcement learning (RL) has led to many recent and groundbreaking advances. However, these advances have often come at the cost of both increased scale in the underlying architectures being trained as well as increased complexity of the RL a
Externí odkaz:
http://arxiv.org/abs/2006.00979
Publikováno v:
Proceedings of The 12th Asian Conference on Machine Learning, PMLR 129:401-416, 2020
This paper extends the notion of learning equilibrium in game theory from matrix games to stochastic games. We introduce Foolproof Cooperative Learning (FCL), an algorithm that converges to a Tit-for-Tat behavior. It allows cooperative strategies whe
Externí odkaz:
http://arxiv.org/abs/1906.09831
In social robotics, robots needs to be able to be understood by humans. Especially in collaborative tasks where they have to share mutual knowledge. For instance, in an educative scenario, learners share their knowledge and they must adapt their beha
Externí odkaz:
http://arxiv.org/abs/1602.06703
Autor:
Jacq, Alexis David
Education is an art close to theater. A teacher is taking a role; he works his speeches and his gestures and he plays with the attention of his audience. But it is harder: more than entertaining, a teacher must shape the skills, the knowledge and the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c048824cdfb95d4d0176546c9ca699a9
Akademický článek
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Publikováno v:
ACM/IEEE International Conference on Human-Robot Interaction; Mar2016, p239-246, 8p
Publikováno v:
ACM/IEEE International Conference on Human-Robot Interaction; Mar2016, p157-164, 8p