12 citations · 12 across the 1 of their papers we have counts for
3 papers
cs.LG2019★ 12 cited
Batch Policy Learning under Constraints
Hoang M. Le, Cameron Voloshin, Yisong Yue
When learning policies for real-world domains, two important questions arise: (i) how to efficiently use pre-collected off-policy, non-optimal behavior data; and (ii) how to mediat…
cs.LG2018
Hierarchical Imitation and Reinforcement Learning
Hoang M. Le, Nan Jiang, Alekh Agarwal +3
We study how to effectively leverage expert feedback to learn sequential decision-making policies. We focus on problems with sparse rewards and long time horizons, which typically…
cs.LG2016
Smooth Imitation Learning for Online Sequence Prediction
Hoang M. Le, Andrew Kang, Yisong Yue +1
We study the problem of smooth imitation learning for online sequence prediction, where the goal is to train a policy that can smoothly imitate demonstrated behavior in a dynamic a…