3 papers
cs.LG2026
Representation Learning Enables Scalable Multitask Deep Reinforcement Learning
Johan Obando-Ceron, Lu Li, Scott Fujimoto +3
Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge. While recent advances in model-based RL achieve strong performance, they rely on plan…
cs.HC2023
Explore 3D Dance Generation via Reward Model from Automatically-Ranked Demonstrations
Zilin Wang, Haolin Zhuang, Lu Li +6
This paper presents an Exploratory 3D Dance generation framework, E3D2, designed to address the exploration capability deficiency in existing music-conditioned 3D dance generation…
cs.LG2023
Distance-rank Aware Sequential Reward Learning for Inverse Reinforcement Learning with Sub-optimal Demonstrations
Lu Li, Yuxin Pan, Ruobing Chen +4
Inverse reinforcement learning (IRL) aims to explicitly infer an underlying reward function based on collected expert demonstrations. Considering that obtaining expert demonstratio…