9 citations · 25 across the 5 of their papers we have counts for
4 papers · 1 filter
When to Update Your Model: Constrained Model-based Reinforcement Learning
Tianying Ji, Yu Luo, Fuchun Sun +3
Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization a…
Adversarial Option-Aware Hierarchical Imitation Learning
Mingxuan Jing, Wenbing Huang, Fuchun Sun +4
It has been a challenge to learning skills for an agent from long-horizon unannotated demonstrations. Existing approaches like Hierarchical Imitation Learning(HIL) are prone to com…
Reinforcement Learning from Imperfect Demonstrations under Soft Expert Guidance
Mingxuan Jing, Xiaojian Ma, Wenbing Huang +4
In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations…
Learning and Inferring Movement with Deep Generative Model
Mingxuan Jing, Xiaojian Ma, Fuchun Sun +1
Learning and inference movement is a very challenging problem due to its high dimensionality and dependency to varied environments or tasks. In this paper, we propose an effective…