6 papers
Multi-Agent Reinforcement Learning via Agent-Specific Preference
Ni Mu, Yao Luan, Yiqin Yang +1
Multi-agent reinforcement learning (MARL) is a powerful framework for solving complex collaborative tasks, but it relies heavily on well-defined global reward functions. Designing…
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…
Preference-based Multi-Objective Reinforcement Learning
Ni Mu, Yao Luan, Qing-Shan Jia
Multi-objective reinforcement learning (MORL) is a structured approach for optimizing tasks with multiple objectives. However, it often relies on pre-defined reward functions, whic…
Seamless Interaction: Dyadic Audiovisual Motion Modeling and Large-Scale Dataset
Vasu Agrawal, Akinniyi Akinyemi, Kathryn Alvero +81
Human communication involves a complex interplay of verbal and nonverbal signals, essential for conveying meaning and achieving interpersonal goals. To develop socially intelligent…
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…
Towards provable probabilistic safety for scalable embodied AI systems
Linxuan He, Lingxiang Fan, Qing-Shan Jia +13
Embodied AI systems, comprising AI models and physical plants, are increasingly prevalent across various applications. Due to the rarity of system failures, ensuring their safety i…