38 citations · 141 across the 21 of their papers we have counts for
22 papers
Population-size-Aware Policy Optimization for Mean-Field Games
Pengdeng Li, Xinrun Wang, Shuxin Li +2
In this work, we attempt to bridge the two fields of finite-agent and infinite-agent games, by studying how the optimal policies of agents evolve with the number of agents (populat…
Offline Equilibrium Finding
Shuxin Li, Xinrun Wang, Youzhi Zhang +4
Offline reinforcement learning (offline RL) is an emerging field that has recently begun gaining attention across various application domains due to its ability to learn strategies…
A Unified Perspective on Deep Equilibrium Finding
Xinrun Wang, Jakub Cerny, Shuxin Li +4
Extensive-form games provide a versatile framework for modeling interactions of multiple agents subjected to imperfect observations and stochastic events. In recent years, two para…
Solving Large-Scale Extensive-Form Network Security Games via Neural Fictitious Self-Play
Wanqi Xue, Youzhi Zhang, Shuxin Li +3
Securing networked infrastructures is important in the real world. The problem of deploying security resources to protect against an attacker in networked domains can be modeled as…
CFR-MIX: Solving Imperfect Information Extensive-Form Games with Combinatorial Action Space
Shuxin Li, Youzhi Zhang, Xinrun Wang +2
In many real-world scenarios, a team of agents coordinate with each other to compete against an opponent. The challenge of solving this type of game is that the team's joint action…
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
Wei Qiu, Xinrun Wang, Runsheng Yu +5
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (…