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pro vyhledávání: '"Staroverov, Aleksey"'
Autor:
Staroverov, Aleksey, Alhaddad, Muhammad, Narendra, Aditya, Mironov, Konstantin, Panov, Aleksandr
We address a task of local trajectory planning for the mobile robot in the presence of static and dynamic obstacles. Local trajectory is obtained as a numerical solution of the Model Predictive Control (MPC) problem. Collision avoidance may be provid
Externí odkaz:
http://arxiv.org/abs/2410.06819
Model predictive control (MPC) may provide local motion planning for mobile robotic platforms. The challenging aspect is the analytic representation of collision cost for the case when both the obstacle map and robot footprint are arbitrary. We propo
Externí odkaz:
http://arxiv.org/abs/2310.16362
This work studies object goal navigation task, which involves navigating to the closest object related to the given semantic category in unseen environments. Recent works have shown significant achievements both in the end-to-end Reinforcement Learni
Externí odkaz:
http://arxiv.org/abs/2109.09512
Autor:
Skrynnik, Alexey, Staroverov, Aleksey, Aitygulov, Ermek, Aksenov, Kirill, Davydov, Vasilii, Panov, Aleksandr I.
Currently, deep reinforcement learning (RL) shows impressive results in complex gaming and robotic environments. Often these results are achieved at the expense of huge computational costs and require an incredible number of episodes of interaction b
Externí odkaz:
http://arxiv.org/abs/2006.09939
Autor:
Skrynnik, Alexey, Staroverov, Aleksey, Aitygulov, Ermek, Aksenov, Kirill, Davydov, Vasilii, Panov, Aleksandr I.
We present Hierarchical Deep Q-Network (HDQfD) that took first place in the MineRL competition. HDQfD works on imperfect demonstrations and utilizes the hierarchical structure of expert trajectories. We introduce the procedure of extracting an effect
Externí odkaz:
http://arxiv.org/abs/1912.08664
Autor:
Skrynnik, Alexey, Staroverov, Aleksey, Aitygulov, Ermek, Aksenov, Kirill, Davydov, Vasilii, Panov, Aleksandr I.
Publikováno v:
In Knowledge-Based Systems 22 April 2021 218
Autor:
Skrynnik, Alexey, Staroverov, Aleksey, Aitygulov, Ermek, Aksenov, Kirill, Davydov, Vasilii, Panov, Aleksandr I.
Publikováno v:
In Cognitive Systems Research January 2021 65:74-78
Akademický článek
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Autor:
Aleksandr I. Panov, Staroverov Aleksey
Publikováno v:
Studies in Computational Intelligence ISBN: 9783030304249
Hierarchies are used in reinforcement learning to increase learning speed in sparse reward tasks. In this kind of tasks, the main problem is elapsed time, required for the initial policy to reach the goal during the first steps. Hierarchies can split
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5e89a3cad17a5a925372d5f331a5c207
https://doi.org/10.1007/978-3-030-30425-6_6
https://doi.org/10.1007/978-3-030-30425-6_6