52 citations · 71 across the 3 of their papers we have counts for
9 papers
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…
Efficient Exploration via State Marginal Matching
Lisa Lee, Benjamin Eysenbach, Emilio Parisotto +3
Exploration is critical to a reinforcement learning agent's performance in its given environment. Prior exploration methods are often based on using heuristic auxiliary predictions…
Embodied Multimodal Multitask Learning
Devendra Singh Chaplot, Lisa Lee, Ruslan Salakhutdinov +2
Recent efforts on training visual navigation agents conditioned on language using deep reinforcement learning have been successful in learning policies for different multimodal tas…
On the Complexity of Exploration in Goal-Driven Navigation
Maruan Al-Shedivat, Lisa Lee, Ruslan Salakhutdinov +1
Building agents that can explore their environments intelligently is a challenging open problem. In this paper, we make a step towards understanding how a hierarchical design of th…