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pro vyhledávání: '"Gong, JianXing"'
In Markov games, playing against non-stationary opponents with learning ability is still challenging for reinforcement learning (RL) agents, because the opponents can evolve their policies concurrently. This increases the complexity of the learning t
Externí odkaz:
http://arxiv.org/abs/2005.12553
Publikováno v:
In Knowledge-Based Systems 22 April 2022 242
Publikováno v:
In Swarm and Evolutionary Computation August 2021 65
Publikováno v:
In Neurocomputing 14 March 2020 381:167-176
Publikováno v:
In Knowledge-Based Systems 15 October 2019 182
Publikováno v:
In Neurocomputing 21 April 2019 338:307-320
Publikováno v:
IFAC-PapersOnLine. 55:213-218
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::526e36309d8bba2f6b3a71763fd57857
https://doi.org/10.1016/j.ifacol.2022.05.037
https://doi.org/10.1016/j.ifacol.2022.05.037
Publikováno v:
2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e17fda1feecfc4c369b7146fc8a6b3d
https://doi.org/10.1109/icetci55101.2022.9832048
https://doi.org/10.1109/icetci55101.2022.9832048
Akademický článek
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Publikováno v:
IEEE Access, Vol 7, Pp 53296-53304 (2019)
Many deep learning architectures have been employed to model the semantic compositionality for text sequences, requiring a huge amount of supervised data for parameters training, making it unfeasible in situations where numerous annotated samples are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76bac5588efacce74da82421b611f21a
https://doi.org/10.1109/access.2019.2911850
https://doi.org/10.1109/access.2019.2911850