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pro vyhledávání: '"Zhang, Pushi"'
The game industry is challenged to cope with increasing growth in demand and game complexity while maintaining acceptable quality standards for released games. Classic approaches solely depending on human efforts for quality assurance and game testin
Externí odkaz:
http://arxiv.org/abs/2308.09289
With strong capabilities of reasoning and a broad understanding of the world, Large Language Models (LLMs) have demonstrated immense potential in building versatile embodied decision-making agents capable of executing a wide array of tasks. Neverthel
Externí odkaz:
http://arxiv.org/abs/2305.15695
Autor:
Chen, Xiaoyu, Zhu, Xiangming, Zheng, Yufeng, Zhang, Pushi, Zhao, Li, Cheng, Wenxue, Cheng, Peng, Xiong, Yongqiang, Qin, Tao, Chen, Jianyu, Liu, Tie-Yan
One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluctuated bandwidth in congestion control. Existing works on adaptation to
Externí odkaz:
http://arxiv.org/abs/2212.12735
A growing trend for value-based reinforcement learning (RL) algorithms is to capture more information than scalar value functions in the value network. One of the most well-known methods in this branch is distributional RL, which models return distri
Externí odkaz:
http://arxiv.org/abs/2110.13578
Publikováno v:
In Neurocomputing 28 April 2021 434:194-202