Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Han, Beining"'
Autor:
Raistrick, Alexander, Mei, Lingjie, Kayan, Karhan, Yan, David, Zuo, Yiming, Han, Beining, Wen, Hongyu, Parakh, Meenal, Alexandropoulos, Stamatis, Lipson, Lahav, Ma, Zeyu, Deng, Jia
We introduce Infinigen Indoors, a Blender-based procedural generator of photorealistic indoor scenes. It builds upon the existing Infinigen system, which focuses on natural scenes, but expands its coverage to indoor scenes by introducing a diverse li
Externí odkaz:
http://arxiv.org/abs/2406.11824
Fetching, which includes approaching, grasping, and retrieving, is a critical challenge for robot manipulation tasks. Existing methods primarily focus on table-top scenarios, which do not adequately capture the complexities of environments where both
Externí odkaz:
http://arxiv.org/abs/2406.11793
Autor:
Raistrick, Alexander, Lipson, Lahav, Ma, Zeyu, Mei, Lingjie, Wang, Mingzhe, Zuo, Yiming, Kayan, Karhan, Wen, Hongyu, Han, Beining, Wang, Yihan, Newell, Alejandro, Law, Hei, Goyal, Ankit, Yang, Kaiyu, Deng, Jia
We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from scratch via randomized mathematical rules, using no external sour
Externí odkaz:
http://arxiv.org/abs/2306.09310
Publikováno v:
NeurIPS2021
Deep Reinforcement Learning (RL) is successful in solving many complex Markov Decision Processes (MDPs) problems. However, agents often face unanticipated environmental changes after deployment in the real world. These changes are often spurious and
Externí odkaz:
http://arxiv.org/abs/2110.14248
Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation. Its variants in the deep Q-learning paradigm have shown great promise in producing reliable value p
Externí odkaz:
http://arxiv.org/abs/2109.14419
We study deep reinforcement learning (RL) algorithms with delayed rewards. In many real-world tasks, instant rewards are often not readily accessible or even defined immediately after the agent performs actions. In this work, we first formally define
Externí odkaz:
http://arxiv.org/abs/2106.11854
Multi-agent policy gradient (MAPG) methods recently witness vigorous progress. However, there is a significant performance discrepancy between MAPG methods and state-of-the-art multi-agent value-based approaches. In this paper, we investigate causes
Externí odkaz:
http://arxiv.org/abs/2007.12322
Value factorization is a popular and promising approach to scaling up multi-agent reinforcement learning in cooperative settings, which balances the learning scalability and the representational capacity of value functions. However, the theoretical u
Externí odkaz:
http://arxiv.org/abs/2006.00587
Akademický článek
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Autor:
HAN Beining, LU Changfen
Publikováno v:
Journal of Physical Education / Tiyu Xuekan; Nov2023, Vol. 30 Issue 6, p110-117, 8p
