6 papers
Model-Based Reinforcement Learning for Heterogeneous Multi-Robot Task Assignment Under Distribution Shifts
Daniel Garces, Sara Castro, Adrian Haimovich +2
Heterogeneous multi-robot service systems must assign requests to compatible robots, construct feasible schedules, and adapt as new tasks arrive online. Historical data can help an…
Operational Reliability of Deadline-Constrained Task Assignment: Stability Characterization and Adversarial Routing
Roee M. Francos, Daniel Garces, Orhan Eren Akgün +2
Automated task-assignment systems often serve stochastic tasks subject to finite deadlines. In these settings, conventional backlog-based stability can be misleading: finite task l…
Provably Stable Multi-Agent Routing with Bounded-Delay Adversaries in the Decision Loop
Roee M. Francos, Daniel Garces, Stephanie Gil
In this work, we are interested in studying multi-agent routing settings, where adversarial agents are part of the assignment and decision loop, degrading the performance of the fl…
Pro-Routing: Proactive Routing of Autonomous Multi-Capacity Robots for Pickup-and-Delivery Tasks
Daniel Garces, Stephanie Gil
We consider a multi-robot setting, where we have a fleet of multi-capacity autonomous robots that must service spatially distributed pickup-and-delivery requests with fixed maximum…
Data-Efficient Multi-Agent Spatial Planning with LLMs
Huangyuan Su, Aaron Walsman, Daniel Garces +2
In this project, our goal is to determine how to leverage the world-knowledge of pretrained large language models for efficient and robust learning in multiagent decision making. W…
Approximate Multiagent Reinforcement Learning for On-Demand Urban Mobility Problem on a Large Map (extended version)
Daniel Garces, Sushmita Bhattacharya, Dimitri Bertsekas +1
In this paper, we focus on the autonomous multiagent taxi routing problem for a large urban environment where the location and number of future ride requests are unknown a-priori,…