Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Alegre, Lucas N."'
Autor:
Huang, Shengyi, Gallouédec, Quentin, Felten, Florian, Raffin, Antonin, Dossa, Rousslan Fernand Julien, Zhao, Yanxiao, Sullivan, Ryan, Makoviychuk, Viktor, Makoviichuk, Denys, Danesh, Mohamad H., Roumégous, Cyril, Weng, Jiayi, Chen, Chufan, Rahman, Md Masudur, Araújo, João G. M., Quan, Guorui, Tan, Daniel, Klein, Timo, Charakorn, Rujikorn, Towers, Mark, Berthelot, Yann, Mehta, Kinal, Chakraborty, Dipam, KG, Arjun, Charraut, Valentin, Ye, Chang, Liu, Zichen, Alegre, Lucas N., Nikulin, Alexander, Hu, Xiao, Liu, Tianlin, Choi, Jongwook, Yi, Brent
In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curves are rarely available. As a result, it is usually necessary to repro
Externí odkaz:
http://arxiv.org/abs/2402.03046
Multi-objective reinforcement learning (MORL) algorithms tackle sequential decision problems where agents may have different preferences over (possibly conflicting) reward functions. Such algorithms often learn a set of policies (each optimized for a
Externí odkaz:
http://arxiv.org/abs/2301.07784
In many real-world applications, reinforcement learning (RL) agents might have to solve multiple tasks, each one typically modeled via a reward function. If reward functions are expressed linearly, and the agent has previously learned a set of polici
Externí odkaz:
http://arxiv.org/abs/2206.11326
Publikováno v:
Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems. 2021. 97-105
Non-stationary environments are challenging for reinforcement learning algorithms. If the state transition and/or reward functions change based on latent factors, the agent is effectively tasked with optimizing a behavior that maximizes performance o
Externí odkaz:
http://arxiv.org/abs/2105.09452
Publikováno v:
PeerJ Computer Science 2021
In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traf
Externí odkaz:
http://arxiv.org/abs/2004.04778
Akademický článek
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Publikováno v:
Weber, Aline Alegre, Lucas N. Tørresen, Jim Castro da Silva, Bruno . Parameterized Melody Generation with Autoencoders and Temporally-Consistent Noise. Music Proceedings of the International Conference on New Interfaces for Musical Expression. 2019, 174-179. Porto Alegre: Universidade Federal do Rio Grande do Sul
Externí odkaz:
http://hdl.handle.net/10852/77398
https://www.duo.uio.no/bitstream/handle/10852/77398/2/nime2019_paper035.pdf
https://www.duo.uio.no/bitstream/handle/10852/77398/2/nime2019_paper035.pdf
Autor:
Ziemke, Theresa1 (AUTHOR) tziemke@vsp.tu-berlin.de, Alegre, Lucas N.2 (AUTHOR) lnalegre@inf.ufrgs.br, Bazzan, Ana L.C.2 (AUTHOR) bazzan@inf.ufrgs.br, Lujak, Marin (AUTHOR), Dusparic, Ivana (AUTHOR), Klügl, Franziska (AUTHOR), Vizzari, Giuseppe (AUTHOR)
Publikováno v:
AI Communications. 2021, Vol. 34 Issue 1, p89-103. 15p.
Akademický článek
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Akademický článek
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