Showing 2024Show all
3 papers · 1 filter
eess.SY2024
Compositional Planning for Logically Constrained Multi-Agent Markov Decision Processes
Krishna C. Kalagarla, Matthew Low, Rahul Jain +2
Designing control policies for large, distributed systems is challenging, especially in the context of critical, temporal logic based specifications (e.g., safety) that must be met…
cs.LG2024
Markov Balance Satisfaction Improves Performance in Strictly Batch Offline Imitation Learning
Rishabh Agrawal, Nathan Dahlin, Rahul Jain +1
Imitation learning (IL) is notably effective for robotic tasks where directly programming behaviors or defining optimal control costs is challenging. In this work, we address a sce…
cs.LG2024
Pure Exploration for Constrained Best Mixed Arm Identification with a Fixed Budget
Dengwang Tang, Rahul Jain, Ashutosh Nayyar +1
In this paper, we introduce the constrained best mixed arm identification (CBMAI) problem with a fixed budget. This is a pure exploration problem in a stochastic finite armed bandi…