10 citations · 33 across the 33 of their papers we have counts for
7 papers · 1 filter
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
Jasmine Bayrooti, Carl Henrik Ek, Amanda Prorok
Learning complex robot behavior through interactions with the environment necessitates principled exploration. Effective strategies should prioritize exploring regions of the state…
Language-Conditioned Offline RL for Multi-Robot Navigation
Steven Morad, Ajay Shankar, Jan Blumenkamp +1
We present a method for developing navigation policies for multi-robot teams that interpret and follow natural language instructions. We condition these policies on embeddings from…
CoDreamer: Communication-Based Decentralised World Models
Edan Toledo, Amanda Prorok
Sample efficiency is a critical challenge in reinforcement learning. Model-based RL has emerged as a solution, but its application has largely been confined to single-agent scenari…
Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning
Matteo Bettini, Ryan Kortvelesy, Amanda Prorok
The study of behavioral diversity in Multi-Agent Reinforcement Learning (MARL) is a nascent yet promising field. In this context, the present work deals with the question of how to…
The Cambridge RoboMaster: An Agile Multi-Robot Research Platform
Jan Blumenkamp, Ajay Shankar, Matteo Bettini +2
Compact robotic platforms with powerful compute and actuation capabilities are key enablers for practical, real-world deployments of multi-agent research. This article introduces a…
CoViS-Net: A Cooperative Visual Spatial Foundation Model for Multi-Robot Applications
Jan Blumenkamp, Steven Morad, Jennifer Gielis +1
Autonomous robot operation in unstructured environments is often underpinned by spatial understanding through vision. Systems composed of multiple concurrently operating robots add…
Co-Optimizing Reconfigurable Environments and Policies for Decentralized Multi-Agent Navigation
Zhan Gao, Guang Yang, Amanda Prorok
This work views the multi-agent system and its surrounding environment as a co-evolving system, where the behavior of one affects the other. The goal is to take both agent actions…