88 citations · 229 across the 15 of their papers we have counts for
3 papers · 1 filter
f-IRL: Inverse Reinforcement Learning via State Marginal Matching
Tianwei Ni, Harshit Sikchi, Yufei Wang +3
Imitation learning is well-suited for robotic tasks where it is difficult to directly program the behavior or specify a cost for optimal control. In this work, we propose a method…
Weakly-Supervised Reinforcement Learning for Controllable Behavior
Lisa Lee, Benjamin Eysenbach, Ruslan Salakhutdinov +2
Reinforcement learning (RL) is a powerful framework for learning to take actions to solve tasks. However, in many settings, an agent must winnow down the inconceivably large space…
Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling
Yaqiong Li, Xuhui Fan, Ling Chen +3
The Dirichlet Belief Network~(DirBN) has been recently proposed as a promising approach in learning interpretable deep latent representations for objects. In this work, we leverage…