3 citations · 8 across the 10 of their papers we have counts for
10 papers
CODA: Coordination via On-Policy Diffusion for Multi-Agent Offline Reinforcement Learning
Marcel Hedman, Kale-ab Abebe Tessera, Juan Claude Formanek +5
Offline multi-agent reinforcement learning (MARL) enables policy learning from fixed datasets, but is prone to coordination failure: agents trained on static, off-policy data conve…
Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics
Leonard Hinckeldey, Elliot Fosong, Rimvydas Rubavicius +6
As embodied autonomous systems capable of assisting humans in daily activities remain a major goal for robotics, efficient and appropriate reinforcement learning (RL) simulation te…
Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition
Elliot Fosong, Arrasy Rahman, Ignacio Carlucho +1
Training a team to complete a complex task via multi-agent reinforcement learning can be difficult due to challenges such as policy search in a large joint policy space, and non-st…
Deep Reinforcement Learning for Multi-Agent Interaction
Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho +14
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Tow…
Few-Shot Teamwork
Elliot Fosong, Arrasy Rahman, Ignacio Carlucho +1
We propose the novel few-shot teamwork (FST) problem, where skilled agents trained in a team to complete one task are combined with skilled agents from different tasks, and togethe…
Cooperative Marine Operations via Ad Hoc Teams
Ignacio Carlucho, Arrasy Rahman, William Ard +3
While research in ad hoc teamwork has great potential for solving real-world robotic applications, most developments so far have been focusing on environments with simple dynamics.…
Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity
Arrasy Rahman, Elliot Fosong, Ignacio Carlucho +1
Ad hoc teamwork (AHT) is the challenge of designing a robust learner agent that effectively collaborates with unknown teammates without prior coordination mechanisms. Early approac…
A Survey of Ad Hoc Teamwork Research
Reuth Mirsky, Ignacio Carlucho, Arrasy Rahman +5
Ad hoc teamwork is the research problem of designing agents that can collaborate with new teammates without prior coordination. This survey makes a two-fold contribution: First, it…
Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles
Josiah P. Hanna, Arrasy Rahman, Elliot Fosong +5
Recognising the goals or intentions of observed vehicles is a key step towards predicting the long-term future behaviour of other agents in an autonomous driving scenario. When the…
Towards Quantum-Secure Authentication and Key Agreement via Abstract Multi-Agent Interaction
Ibrahim H. Ahmed, Josiah P. Hanna, Elliot Fosong +1
Current methods for authentication and key agreement based on public-key cryptography are vulnerable to quantum computing. We propose a novel approach based on artificial intellige…