1 code implementation • 10 Dec 2023 • Yihan Hu, Yiheng Lin, Wei Wang, Yao Zhao, Yunchao Wei, Humphrey Shi
However, the presence of high computational overhead and the inconsistency of noise sampling between the training and inference processes pose significant obstacles to achieving this goal.
Ranked #1 on Image Matting on Distinctions-646
1 code implementation • 8 Mar 2023 • Zhaoyi Zhou, Zaiwei Chen, Yiheng Lin, Adam Wierman
The algorithm is scalable since each agent uses only local information and does not need access to the global state.
no code implementations • 30 Nov 2022 • Yizhou Zhang, Guannan Qu, Pan Xu, Yiheng Lin, Zaiwei Chen, Adam Wierman
In particular, we show that, despite restricting each agent's attention to only its $\kappa$-hop neighborhood, the agents are able to learn a policy with an optimality gap that decays polynomially in $\kappa$.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 2 Jun 2022 • Tongxin Li, Ruixiao Yang, Guannan Qu, Yiheng Lin, Steven Low, Adam Wierman
Machine-learned black-box policies are ubiquitous for nonlinear control problems.
1 code implementation • 12 Apr 2022 • Sungho Shin, Yiheng Lin, Guannan Qu, Adam Wierman, Mihai Anitescu
This paper studies the trade-off between the degree of decentralization and the performance of a distributed controller in a linear-quadratic control setting.
no code implementations • 29 Oct 2021 • Weici Pan, Guanya Shi, Yiheng Lin, Adam Wierman
We study a variant of online optimization in which the learner receives $k$-round $\textit{delayed feedback}$ about hitting cost and there is a multi-step nonlinear switching cost, i. e., costs depend on multiple previous actions in a nonlinear manner.
no code implementations • NeurIPS 2020 • Guannan Qu, Yiheng Lin, Adam Wierman, Na Li
It has long been recognized that multi-agent reinforcement learning (MARL) faces significant scalability issues due to the fact that the size of the state and action spaces are exponentially large in the number of agents.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • NeurIPS 2021 • Yiheng Lin, Guannan Qu, Longbo Huang, Adam Wierman
We study multi-agent reinforcement learning (MARL) in a stochastic network of agents.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • NeurIPS 2020 • Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman
This paper presents competitive algorithms for a novel class of online optimization problems with memory.
no code implementations • 10 Nov 2019 • Yiheng Lin, Gautam Goel, Adam Wierman
In this work, we give two general sufficient conditions that specify a relationship between the hitting and movement costs which guarantees that a new algorithm, Synchronized Fixed Horizon Control (SFHC), provides a $1+O(1/w)$ competitive ratio, where $w$ is the number of predictions available to the learner.
no code implementations • NeurIPS 2019 • Gautam Goel, Yiheng Lin, Haoyuan Sun, Adam Wierman
We prove a new lower bound on the competitive ratio of any online algorithm in the setting where the costs are $m$-strongly convex and the movement costs are the squared $\ell_2$ norm.