From the 1 of 5 linked papers with an AI index.
5 papers
AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization
Shengda Gu, Kai Li, Xinyi Ke +3
AutoPref uses a large language model to automatically discover and compose pairwise loss and weighting programs that define preference objectives for neural combinatorial optimizat…
Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork
Yuheng Jing, Kai Li, Ziwen Zhang +8
In-Context Reinforcement Learning (ICRL) has enabled foundation agents to adapt instantaneously to novel tasks, yet its efficacy in Ad-Hoc Teamwork (AHT)-where coordination with un…
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…