62 citations · 78 across the 6 of their papers we have counts for
6 papers
LTL-Constrained Policy Optimization with Cycle Experience Replay
Ameesh Shah, Cameron Voloshin, Chenxi Yang +3
Linear Temporal Logic (LTL) offers a precise means for constraining the behavior of reinforcement learning agents. However, in many settings where both satisfaction and optimality…
Eventual Discounting Temporal Logic Counterfactual Experience Replay
Cameron Voloshin, Abhinav Verma, Yisong Yue
Linear temporal logic (LTL) offers a simplified way of specifying tasks for policy optimization that may otherwise be difficult to describe with scalar reward functions. However, t…
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…
Minimax Model Learning
Cameron Voloshin, Nan Jiang, Yisong Yue
We present a novel off-policy loss function for learning a transition model in model-based reinforcement learning. Notably, our loss is derived from the off-policy policy evaluatio…
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…