Zobrazeno 1 - 10
of 320
pro vyhledávání: '"Hu, Jifeng"'
Autor:
Huang, Sili, Hu, Jifeng, Yang, Zhejian, Yang, Liwei, Luo, Tao, Chen, Hechang, Sun, Lichao, Yang, Bo
Recent works have shown the remarkable superiority of transformer models in reinforcement learning (RL), where the decision-making problem is formulated as sequential generation. Transformer-based agents could emerge with self-improvement in online e
Externí odkaz:
http://arxiv.org/abs/2406.00079
In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts. Recent works demonstrated that in-context RL could emerge with self-improvement in a trial-an
Externí odkaz:
http://arxiv.org/abs/2405.20692
Long-lived particles (LLPs) provide an unambiguous signal for physics beyond the Standard Model (BSM). They have a distinct detector signature, with decay lengths corresponding to lifetimes of around nanoseconds or longer. Lepton colliders allow LLP
Externí odkaz:
http://arxiv.org/abs/2401.05094
Autor:
Huang, Sili, Sun, Yanchao, Hu, Jifeng, Guo, Siyuan, Chen, Hechang, Chang, Yi, Sun, Lichao, Yang, Bo
In visual-based Reinforcement Learning (RL), agents often struggle to generalize well to environmental variations in the state space that were not observed during training. The variations can arise in both task-irrelevant features, such as background
Externí odkaz:
http://arxiv.org/abs/2310.05086
Autor:
Guo, Siyuan, Sun, Yanchao, Hu, Jifeng, Huang, Sili, Chen, Hechang, Piao, Haiyin, Sun, Lichao, Chang, Yi
Offline reinforcement learning (RL) provides a promising solution to learning an agent fully relying on a data-driven paradigm. However, constrained by the limited quality of the offline dataset, its performance is often sub-optimal. Therefore, it is
Externí odkaz:
http://arxiv.org/abs/2306.07541
Autor:
Hu, Jifeng, Sun, Yanchao, Huang, Sili, Guo, SiYuan, Chen, Hechang, Shen, Li, Sun, Lichao, Chang, Yi, Tao, Dacheng
Recent works have shown the potential of diffusion models in computer vision and natural language processing. Apart from the classical supervised learning fields, diffusion models have also shown strong competitiveness in reinforcement learning (RL)
Externí odkaz:
http://arxiv.org/abs/2306.04875
We study the nature of the hidden charm pentaquarks, i.e. the $P_c(4312)$, $P_c(4440)$ and $P_c(4457)$, with a neural network approach in pionless effective field theory. In this framework, the normal $\chi^2$ fitting approach cannot distinguish the
Externí odkaz:
http://arxiv.org/abs/2301.05364
Multi-agent reinforcement learning has drawn increasing attention in practice, e.g., robotics and automatic driving, as it can explore optimal policies using samples generated by interacting with the environment. However, high reward uncertainty stil
Externí odkaz:
http://arxiv.org/abs/2210.07636
Machine learning is a novel and powerful technology and has been widely used in various science topics. We demonstrate a machine-learning based approach built by a set of general metrics and rules inspired by physics. Taking advantages of physical co
Externí odkaz:
http://arxiv.org/abs/2208.03165
We analyzed the invariant mass spectrum of near-threshold exotic states for one-channel candidates with a deep neural network. It can extract the scattering length and effective range, which would shed light on the nature of given states, from the ex
Externí odkaz:
http://arxiv.org/abs/2202.04929