9 papers
COLLIE: Guiding Skill Discovery in Semantically Coherent Latent Space
Yao Luan, Ni Mu, Hanfei Ge +3
Unsupervised skill discovery (USD) aims to learn diverse behaviors without reward functions, but often results in task-irrelevant or hazardous behaviors due to uniform exploration.…
GUIDE: Interpretable GUI Agent Evaluation via Hierarchical Diagnosis
Yuwen Zhai, Runze Li, Liang Wang +6
Evaluating GUI agents presents a distinct challenge: trajectories are long, visually grounded, and open-ended, yet evaluation must be both accurate and interpretable. Existing appr…
OPRIDE: Offline Preference-based Reinforcement Learning via In-Dataset Exploration
Yiqin Yang, Hao Hu, Yihuan Mao +10
Preference-based reinforcement learning (PbRL) can help avoid sophisticated reward designs and align better with human intentions, showing great promise in various real-world appli…
MrCoM: A Meta-Regularized World-Model Generalizing Across Multi-Scenarios
Xuantang Xiong, Ni Mu, Runpeng Xie +8
Model-based reinforcement learning (MBRL) is a crucial approach to enhance the generalization capabilities and improve the sample efficiency of RL algorithms. However, current MBRL…
STAIR: Addressing Stage Misalignment through Temporal-Aligned Preference Reinforcement Learning
Yao Luan, Ni Mu, Yiqin Yang +2
Preference-based reinforcement learning (PbRL) bypasses complex reward engineering by learning rewards directly from human preferences, enabling better alignment with human intenti…
An Efficient Data-Driven Framework for Linear Quadratic Output Feedback Control
Jun Xie, Yuan-Hua Ni, Yiqin Yang +1
Linear quadratic regulator with unmeasurable states and unknown system matrix parameters better aligns with practical scenarios. However, for this problem, balancing the optimality…
SC2Arena and StarEvolve: Benchmark and Self-Improvement Framework for LLMs in Complex Decision-Making Tasks
Pengbo Shen, Yaqing Wang, Ni Mu +8
Evaluating large language models (LLMs) in complex decision-making is essential for advancing AI's ability for strategic planning and real-time adaptation. However, existing benchm…
CLARIFY: Contrastive Preference Reinforcement Learning for Untangling Ambiguous Queries
Ni Mu, Hao Hu, Xiao Hu +3
Preference-based reinforcement learning (PbRL) bypasses explicit reward engineering by inferring reward functions from human preference comparisons, enabling better alignment with…
S-EPOA: Overcoming the Indistinguishability of Segments with Skill-Driven Preference-Based Reinforcement Learning
Ni Mu, Yao Luan, Yiqin Yang +2
Preference-based reinforcement learning (PbRL) stands out by utilizing human preferences as a direct reward signal, eliminating the need for intricate reward engineering. However,…