124 citations · 953 across the 47 of their papers we have counts for
9 papers · 1 filter
Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games
Zuyue Fu, Zhuoran Yang, Yongxin Chen +1
We study discrete-time mean-field Markov games with infinite numbers of agents where each agent aims to minimize its ergodic cost. We consider the setting where the agents have ide…
Sample Elicitation
Jiaheng Wei, Zuyue Fu, Yang Liu +3
It is important to collect credible training samples for building data-intensive learning systems (e.g., a deep learning system). Asking people to report complex distributi…
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence
Lingxiao Wang, Qi Cai, Zhuoran Yang +1
Policy gradient methods with actor-critic schemes demonstrate tremendous empirical successes, especially when the actors and critics are parameterized by neural networks. However,…
Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization
Dongsheng Ding, Xiaohan Wei, Zhuoran Yang +2
We study the policy evaluation problem in multi-agent reinforcement learning where a group of agents, with jointly observed states and private local actions and rewards, collaborat…
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning
Xinyang Yi, Zhaoran Wang, Zhuoran Yang +2
We consider the weakly supervised binary classification problem where the labels are randomly flipped with probability . Although there exist numerous algorithms for this pro…
On the Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost
Zhuoran Yang, Yongxin Chen, Mingyi Hong +1
Despite the empirical success of the actor-critic algorithm, its theoretical understanding lags behind. In a broader context, actor-critic can be viewed as an online alternating up…
A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning
Yixuan Lin, Kaiqing Zhang, Zhuoran Yang +4
This paper considers a distributed reinforcement learning problem in which a network of multiple agents aim to cooperatively maximize the globally averaged return through communica…
On the Global Convergence of Imitation Learning: A Case for Linear Quadratic Regulator
Qi Cai, Mingyi Hong, Yongxin Chen +1
We study the global convergence of generative adversarial imitation learning for linear quadratic regulators, which is posed as minimax optimization. To address the challenges aris…
A Theoretical Analysis of Deep Q-Learning
Jianqing Fan, Zhaoran Wang, Yuchen Xie +1
Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well understood. In this work, we make the first attempt to theoretically und…