2 citations · 2 across the 9 of their papers we have counts for
6 papers · 1 filter
AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization
Shengda Gu, Kai Li, Xinyi Ke +3
Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions costly to obtain. Neural comb…
Deep (Predictive) Discounted Counterfactual Regret Minimization
Hang Xu, Kai Li, Haobo Fu +3
Counterfactual regret minimization (CFR) is a family of algorithms for effectively solving imperfect-information games. To enhance CFR's applicability in large games, researchers u…
Goal-Oriented Skill Abstraction for Offline Multi-Task Reinforcement Learning
Jinmin He, Kai Li, Yifan Zang +4
Offline multi-task reinforcement learning aims to learn a unified policy capable of solving multiple tasks using only pre-collected task-mixed datasets, without requiring any onlin…
Efficient Multi-Task Reinforcement Learning with Cross-Task Policy Guidance
Jinmin He, Kai Li, Yifan Zang +4
Multi-task reinforcement learning endeavors to efficiently leverage shared information across various tasks, facilitating the simultaneous learning of multiple tasks. Existing appr…
Minimizing Weighted Counterfactual Regret with Optimistic Online Mirror Descent
Hang Xu, Kai Li, Bingyun Liu +4
Counterfactual regret minimization (CFR) is a family of algorithms for effectively solving imperfect-information games. It decomposes the total regret into counterfactual regrets,…
OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research
Kai Li, Hang Xu, Enmin Zhao +2
Owning to the unremitting efforts by a few institutes, significant progress has recently been made in designing superhuman AIs in No-limit Texas Hold'em (NLTH), the primary testbed…