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of 44 476
pro vyhledávání: '"Action Selection"'
Multi-label multi-view action recognition aims to recognize multiple concurrent or sequential actions from untrimmed videos captured by multiple cameras. Existing work has focused on multi-view action recognition in a narrow area with strong labels a
Externí odkaz:
http://arxiv.org/abs/2410.03302
In this work, we introduce a strategy that frames the sequential action selection problem for robots in terms of resolving \textit{blocking conditions}, i.e., situations that impede progress on an action en route to a goal. This strategy allows a rob
Externí odkaz:
http://arxiv.org/abs/2409.08410
Autor:
Kim, Sang-Yoon, Lim, Woochang
We are concerned about action selection in the basal ganglia (BG). We quantitatively analyze functions of direct pathway (DP) and indirect pathway (IP) for action selection in a spiking neural network with 3 competing channels. For such quantitative
Externí odkaz:
http://arxiv.org/abs/2404.13888
Autor:
Iturria-Rivera, Pedro Enrique, Gaigalas, Raimundas, Elsayed, Medhat, Bavand, Majid, Ozcan, Yigit, Erol-Kantarci, Melike
Extended Reality (XR) services will revolutionize applications over 5th and 6th generation wireless networks by providing seamless virtual and augmented reality experiences. These applications impose significant challenges on network infrastructure,
Externí odkaz:
http://arxiv.org/abs/2405.15872
Autor:
Chang, Ya-Chien, Gao, Sicun
Reinforcement learning for control over continuous spaces typically uses high-entropy stochastic policies, such as Gaussian distributions, for local exploration and estimating policy gradient to optimize performance. Many robotic control problems dea
Externí odkaz:
http://arxiv.org/abs/2404.01598
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Multi-objective reinforcement learning (MORL) algorithms extend conventional reinforcement learning (RL) to the more general case of problems with multiple, conflicting objectives, represented by vector-valued rewards. Widely-used scalar RL methods s
Externí odkaz:
http://arxiv.org/abs/2402.06266