5 papers
Efficient Last-Iterate Convergence in Regret Minimization via Adaptive Reward Transformation
Hang Ren, Yulin Wu, Shuhan Qi +4
Regret minimization is a powerful method for finding Nash equilibria in Normal-Form Games (NFGs) and Extensive-Form Games (EFGs), but it typically guarantees convergence only for t…
Last-Iterate Convergence in Adaptive Regret Minimization for Approximate Extensive-Form Perfect Equilibrium
Hang Ren, Xiaozhen Sun, Tianzi Ma +2
The Nash Equilibrium (NE) assumes rational play in imperfect-information Extensive-Form Games (EFGs) but fails to ensure optimal strategies for off-equilibrium branches of the game…
Enhanced Equilibria-Solving via Private Information Pre-Branch Structure in Adversarial Team Games
Chen Qiu, Haobo Fu, Kai Li +3
In ex ante coordinated adversarial team games (ATGs), a team competes against an adversary, and the team members are only allowed to coordinate their strategies before the game sta…
Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing
Yizhou Luo, Qiang Wang, Shaohuai Shi +4
Deep learning (DL) has demonstrated significant success across diverse fields, leading to the construction of dedicated GPU accelerators within GPU clusters for high-quality traini…
Combining Counterfactual Regret Minimization with Information Gain to Solve Extensive Games with Unknown Environments
Chen Qiu, Xuan Wang, Tianzi Ma +2
Counterfactual regret minimization (CFR) is an effective algorithm for solving extensive games with imperfect information (IIEGs). However, CFR is only allowed to be applied in kno…