40 citations · 119 across the 19 of their papers we have counts for
10 papers · 1 filter
Approximate Global Convergence of Independent Learning in Multi-Agent Systems
Ruiyang Jin, Zaiwei Chen, Yiheng Lin +2
Independent learning (IL), despite being a popular approach in practice to achieve scalability in large-scale multi-agent systems, usually lacks global convergence guarantees. In t…
Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions
Tongxin Li, Yiheng Lin, Shaolei Ren +1
We study the tradeoff between consistency and robustness in the context of a single-trajectory time-varying Markov Decision Process (MDP) with untrusted machine-learned advice. Our…
Convergence Rates for Localized Actor-Critic in Networked Markov Potential Games
Zhaoyi Zhou, Zaiwei Chen, Yiheng Lin +1
We introduce a class of networked Markov potential games in which agents are associated with nodes in a network. Each agent has its own local potential function, and the reward of…
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning
Yizhou Zhang, Guannan Qu, Pan Xu +3
We study a multi-agent reinforcement learning (MARL) problem where the agents interact over a given network. The goal of the agents is to cooperatively maximize the average of thei…
Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Control
Tongxin Li, Ruixiao Yang, Guannan Qu +3
Machine-learned black-box policies are ubiquitous for nonlinear control problems. Meanwhile, crude model information is often available for these problems from, e.g., linear approx…
Online Optimization with Feedback Delay and Nonlinear Switching Cost
Weici Pan, Guanya Shi, Yiheng Lin +1
We study a variant of online optimization in which the learner receives -round about hitting cost and there is a multi-step nonlinear switching cost,…
Multi-Agent Reinforcement Learning in Stochastic Networked Systems
Yiheng Lin, Guannan Qu, Longbo Huang +1
We study multi-agent reinforcement learning (MARL) in a stochastic network of agents. The objective is to find localized policies that maximize the (discounted) global reward. In g…
Online Optimization with Memory and Competitive Control
Guanya Shi, Yiheng Lin, Soon-Jo Chung +2
This paper presents competitive algorithms for a novel class of online optimization problems with memory. We consider a setting where the learner seeks to minimize the sum of a hit…
Online Optimization with Predictions and Non-convex Losses
Yiheng Lin, Gautam Goel, Adam Wierman
We study online optimization in a setting where an online learner seeks to optimize a per-round hitting cost, which may be non-convex, while incurring a movement cost when changing…
Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization
Gautam Goel, Yiheng Lin, Haoyuan Sun +1
We study online convex optimization in a setting where the learner seeks to minimize the sum of a per-round hitting cost and a movement cost which is incurred when changing decisio…