62 citations · 230 across the 8 of their papers we have counts for
6 papers · 1 filter
Policy Optimization with Linear Temporal Logic Constraints
Cameron Voloshin, Hoang M. Le, Swarat Chaudhuri +1
We study the problem of policy optimization (PO) with linear temporal logic (LTL) constraints. The language of LTL allows flexible description of tasks that may be unnatural to enc…
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
Cameron Voloshin, Hoang M. Le, Nan Jiang +1
We offer an experimental benchmark and empirical study for off-policy policy evaluation (OPE) in reinforcement learning, which is a key problem in many safety critical applications…
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…
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…
Coordinated Multi-Agent Imitation Learning
Hoang M. Le, Yisong Yue, Peter Carr +1
We study the problem of imitation learning from demonstrations of multiple coordinating agents. One key challenge in this setting is that learning a good model of coordination can…
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…