25 citations · 57 across the 5 of their papers we have counts for
11 papers
V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL
Chi Jin, Qinghua Liu, Yuanhao Wang +1
A major challenge of multiagent reinforcement learning (MARL) is the curse of multiagents, where the size of the joint action space scales exponentially with the number of agents.…
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces
Chi Jin, Qinghua Liu, Tiancheng Yu
Modern reinforcement learning (RL) commonly engages practical problems with large state spaces, where function approximation must be deployed to approximate either the value functi…
Provably Efficient Algorithms for Multi-Objective Competitive RL
Tiancheng Yu, Yi Tian, Jingzhao Zhang +1
We study multi-objective reinforcement learning (RL) where an agent's reward is represented as a vector. In settings where an agent competes against opponents, its performance is m…
Online Learning in Unknown Markov Games
Yi Tian, Yuanhao Wang, Tiancheng Yu +1
We study online learning in unknown Markov games, a problem that arises in episodic multi-agent reinforcement learning where the actions of the opponents are unobservable. We show…
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
Qinghua Liu, Tiancheng Yu, Yu Bai +1
Model-based algorithms -- algorithms that explore the environment through building and utilizing an estimated model -- are widely used in reinforcement learning practice and theore…
Near-Optimal Reinforcement Learning with Self-Play
Yu Bai, Chi Jin, Tiancheng Yu
This paper considers the problem of designing optimal algorithms for reinforcement learning in two-player zero-sum games. We focus on self-play algorithms which learn the optimal p…