2 citations · 2 across the 8 of their papers we have counts for
4 papers · 1 filter
Learning Generalizable Behavior via Visual Rewrite Rules
Yiheng Xie, Mingxuan Li, Shangqun Yu +1
Though deep reinforcement learning agents have achieved unprecedented success in recent years, their learned policies can be brittle, failing to generalize to even slight modificat…
Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator
Homer Walke, Daniel Ritter, Carl Trimbach +1
Finite linear temporal logic () is a powerful formal representation for modeling temporal sequences. We address the problem of learning a compact f…
On the (In)Tractability of Reinforcement Learning for LTL Objectives
Cambridge Yang, Michael Littman, Michael Carbin
In recent years, researchers have made significant progress in devising reinforcement-learning algorithms for optimizing linear temporal logic (LTL) objectives and LTL-like objecti…
Coarse-Grained Smoothness for RL in Metric Spaces
Omer Gottesman, Kavosh Asadi, Cameron Allen +3
Principled decision-making in continuous state--action spaces is impossible without some assumptions. A common approach is to assume Lipschitz continuity of the Q-function. We show…