Zobrazeno 1 - 10
of 11 677
pro vyhledávání: '"Belousov , A."'
We tackle the recently introduced benchmark for whole-body humanoid control HumanoidBench using MuJoCo MPC. We find that sparse reward functions of HumanoidBench yield undesirable and unrealistic behaviors when optimized; therefore, we propose a set
Externí odkaz:
http://arxiv.org/abs/2408.00342
For classical knots, there is a concept of (semi)meander diagrams; in this short note we generalize this concept to virtual knots and prove that the classes of meander and semimeander diagrams are universal (this was known for classical knots). We al
Externí odkaz:
http://arxiv.org/abs/2407.07164
We consider noncompact open $SL(2, \mathbb{C})$ spin chain and construct eigenfunctions of $B$-element of monodromy matrix for the simplest case of the chain with one site. The reflection operator appearing in this construction can be used to express
Externí odkaz:
http://arxiv.org/abs/2406.19864
Deep Reinforcement Learning (RL) is well known for being highly sensitive to hyperparameters, requiring practitioners substantial efforts to optimize them for the problem at hand. In recent years, the field of automated Reinforcement Learning (AutoRL
Externí odkaz:
http://arxiv.org/abs/2405.16195
Autor:
Böhm, Alina, Schneider, Tim, Belousov, Boris, Kshirsagar, Alap, Lin, Lisa, Doerschner, Katja, Drewing, Knut, Rothkopf, Constantin A., Peters, Jan
This paper explores active sensing strategies that employ vision-based tactile sensors for robotic perception and classification of fabric textures. We formalize the active sampling problem in the context of tactile fabric recognition and provide an
Externí odkaz:
http://arxiv.org/abs/2403.13701
Autor:
Belousov, Grigory, Loginov, Konstantin
We prove that all general smooth Fano threefolds of Picard rank $3$ and degree $14$ are K-stable, where the generality condition is stated explicitly.
Comment: arXiv admin note: text overlap with arXiv:2206.12208
Comment: arXiv admin note: text overlap with arXiv:2206.12208
Externí odkaz:
http://arxiv.org/abs/2403.03700
The vast majority of Reinforcement Learning methods is largely impacted by the computation effort and data requirements needed to obtain effective estimates of action-value functions, which in turn determine the quality of the overall performance and
Externí odkaz:
http://arxiv.org/abs/2403.02107
Autor:
Shevchenko, Valeriy, Belousov, Nikita, Vasilev, Alexey, Zholobov, Vladimir, Sosedka, Artyom, Semenova, Natalia, Volodkevich, Anna, Savchenko, Andrey, Zaytsev, Alexey
In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets. However, this approach may fail to holistically ref
Externí odkaz:
http://arxiv.org/abs/2402.09766
Living materials adapt their shape to signals from the environment, yet the impact of shape changes on signal processing and the associated feedback dynamics remain unclear. We derive coarse-grained equations for droplets that adjust their interfacia
Externí odkaz:
http://arxiv.org/abs/2402.08664
The article identified 42 cognitive architectures for creating general artificial intelligence (AGI) and proposed a set of interrelated functional blocks that an agent approaching AGI in its capabilities should possess. Since the required set of bloc
Externí odkaz:
http://arxiv.org/abs/2401.06256