no code implementations • 5 Feb 2024 • Qingyuan Wu, Simon Sinong Zhan, YiXuan Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Chao Huang
Reinforcement learning is challenging in delayed scenarios, a common real-world situation where observations and interactions occur with delays.
1 code implementation • 3 Nov 2023 • Simon Sinong Zhan, YiXuan Wang, Qingyuan Wu, Ruochen Jiao, Chao Huang, Qi Zhu
In the context of safe exploration, Reinforcement Learning (RL) has long grappled with the challenges of balancing the tradeoff between maximizing rewards and minimizing safety violations, particularly in complex environments with contact-rich or non-smooth dynamics, and when dealing with high-dimensional pixel observations.
no code implementations • 11 Apr 2022 • Jiayu Yao, Qingyuan Wu, Quan Feng, Songcan Chen
Self-supervised learning (SSL), as a newly emerging unsupervised representation learning paradigm, generally follows a two-stage learning pipeline: 1) learning invariant and discriminative representations with auto-annotation pretext(s), then 2) transferring the representations to assist downstream task(s).
no code implementations • 28 Nov 2021 • Yicheng Zhu, Yiqiao Qiu, Qingyuan Wu, Fu Lee Wang, Yanghui Rao
In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space.
no code implementations • 18 May 2021 • Shuhan Zheng, Zhichao Liang, Youzhi Qu, Qingyuan Wu, Haiyan Wu, Quanying Liu
Here, we propose a physics-based framework of Kuramoto model to investigate oxytocin effects on the phase dynamic neural coupling in DMN and FPN.
no code implementations • 23 Feb 2021 • Yuhui Wang, Qingyuan Wu, Pengcheng He, Xiaoyang Tan
Most of the policy evaluation algorithms are based on the theories of Bellman Expectation and Optimality Equation, which derive two popular approaches - Policy Iteration (PI) and Value Iteration (VI).