7 citations · 26 across the 16 of their papers we have counts for
12 papers · 1 filter
Preference-Based Multi-Agent Reinforcement Learning: Data Coverage and Algorithmic Techniques
Natalia Zhang, Xinqi Wang, Qiwen Cui +3
We initiate the study of Preference-Based Multi-Agent Reinforcement Learning (PbMARL), exploring both theoretical foundations and empirical validations. We define the task as ident…
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation
Yan Dai, Qiwen Cui, Simon S. Du
Markov Games (MG) is an important model for Multi-Agent Reinforcement Learning (MARL). It was long believed that the "curse of multi-agents" (i.e., the algorithmic performance drop…
Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning
Zhaoyi Zhou, Chuning Zhu, Runlong Zhou +3
Off-policy dynamic programming (DP) techniques such as -learning have proven to be important in sequential decision-making problems. In the presence of function approximation, h…
A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning
Haozhe Jiang, Qiwen Cui, Zhihan Xiong +2
We investigate learning the equilibria in non-stationary multi-agent systems and address the challenges that differentiate multi-agent learning from single-agent learning. Specific…
Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation
Qiwen Cui, Kaiqing Zhang, Simon S. Du
We propose a new model, independent linear Markov game, for multi-agent reinforcement learning with a large state space and a large number of agents. This is a class of Markov game…
On Gap-dependent Bounds for Offline Reinforcement Learning
Xinqi Wang, Qiwen Cui, Simon S. Du
This paper presents a systematic study on gap-dependent sample complexity in offline reinforcement learning. Prior work showed when the density ratio between an optimal policy and…
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus
Qiwen Cui, Simon S. Du
This paper considers offline multi-agent reinforcement learning. We propose the strategy-wise concentration principle which directly builds a confidence interval for the joint stra…
When is Offline Two-Player Zero-Sum Markov Game Solvable?
Qiwen Cui, Simon S. Du
We study what dataset assumption permits solving offline two-player zero-sum Markov games. In stark contrast to the offline single-agent Markov decision process, we show that the s…
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
Haque Ishfaq, Qiwen Cui, Viet Nguyen +5
We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm as well as the optimism principle. Unlik…
Near-Optimal Randomized Exploration for Tabular Markov Decision Processes
Zhihan Xiong, Ruoqi Shen, Qiwen Cui +2
We study algorithms using randomized value functions for exploration in reinforcement learning. This type of algorithms enjoys appealing empirical performance. We show that when we…
Minimax Sample Complexity for Turn-based Stochastic Game
Qiwen Cui, Lin F. Yang
The empirical success of Multi-agent reinforcement learning is encouraging, while few theoretical guarantees have been revealed. In this work, we prove that the plug-in solver appr…
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?
Qiwen Cui, Lin F. Yang
It is believed that a model-based approach for reinforcement learning (RL) is the key to reduce sample complexity. However, the understanding of the sample optimality of model-base…