no code implementations • 17 Mar 2024 • Yingru Li, Zhi-Quan Luo
This work advances randomized exploration in reinforcement learning (RL) with function approximation modeled by linear mixture MDPs.
no code implementations • 26 Feb 2024 • Liangqi Liu, Wenqiang Pu, Yingru Li, Bo Jiu, Zhi-Quan Luo
The dynamic competition between radar and jammer systems presents a significant challenge for modern Electronic Warfare (EW), as current active learning approaches still lack sample efficiency and fail to exploit jammer's characteristics.
no code implementations • 16 Feb 2024 • Yingru Li
We introduce the first probabilistic framework tailored for sequential random projection, an approach rooted in the challenges of sequential decision-making under uncertainty.
no code implementations • 10 Feb 2024 • Yingru Li
We present a simple and unified analysis of the Johnson-Lindenstrauss (JL) lemma, a cornerstone in the field of dimensionality reduction critical for managing high-dimensional data.
no code implementations • 7 Feb 2024 • Yingru Li, Liangqi Liu, Wenqiang Pu, Hao Liang, Zhi-Quan Luo
This work tackles the complexities of multi-player scenarios in \emph{unknown games}, where the primary challenge lies in navigating the uncertainty of the environment through bandit feedback alongside strategic decision-making.
no code implementations • 5 Feb 2024 • Yingru Li, Jiawei Xu, Lei Han, Zhi-Quan Luo
To solve complex tasks under resource constraints, reinforcement learning (RL) agents need to be simple, efficient, and scalable, addressing (1) large state spaces and (2) the continuous accumulation of interaction data.
1 code implementation • ICLR 2022 • Ziniu Li, Yingru Li, Yushun Zhang, Tong Zhang, Zhi-Quan Luo
However, it is limited to the case where 1) a good feature is known in advance and 2) this feature is fixed during the training: if otherwise, RLSVI suffers an unbearable computational burden to obtain the posterior samples of the parameter in the $Q$-value function.
1 code implementation • NeurIPS 2019 • Qing Wang, Yingru Li, Jiechao Xiong, Tong Zhang
In deep reinforcement learning, policy optimization methods need to deal with issues such as function approximation and the reuse of off-policy data.
4 code implementations • 24 Feb 2017 • Kun He, Yingru Li, Sucheta Soundarajan, John E. Hopcroft
We introduce a new paradigm that is important for community detection in the realm of network analysis.