125 citations · 159 across the 14 of their papers we have counts for
19 papers
JaxAHT: A JAX-Based Library for Ad Hoc Teamwork
Caroline Wang, Rolando Fernandez, Zelal Su Mustafaoglu +10
As autonomous agents are increasingly deployed, they must be able to coordinate with previously unseen agents, such as humans. This challenge has been formalized as Ad Hoc Teamwork…
Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Diaz +83
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…
VGC-Bench: Towards Mastering Diverse Team Strategies in Competitive Pokémon
Cameron Angliss, Jiaxun Cui, Jiaheng Hu +2
Developing AI agents that can robustly adapt to varying strategic landscapes without retraining is a central challenge in multi-agent learning. Pokémon Video Game Championships (VG…
Sequence Modeling for N-Agent Ad Hoc Teamwork
Caroline Wang, Di Yang Shi, Elad Liebman +3
N-agent ad hoc teamwork (NAHT) is a newly introduced challenge in multi-agent reinforcement learning, where controlled subteams of varying sizes must dynamically collaborate with v…
ROTATE: Regret-driven Open-ended Training for Ad Hoc Teamwork
Caroline Wang, Arrasy Rahman, Benjamin Nativi +4
Learning to collaborate with previously unseen partners is a fundamental generalization challenge, known as Ad Hoc Teamwork (AHT). Existing methods often adopt a two-stage pipeline…
HyperMARL: Adaptive Hypernetworks for Multi-Agent RL
Kale-ab Abebe Tessera, Arrasy Rahman, Amos Storkey +1
Adaptive cooperation in multi-agent reinforcement learning (MARL) requires policies to express homogeneous, specialised, or mixed behaviours, yet achieving this adaptivity remains…
N-Agent Ad Hoc Teamwork
Caroline Wang, Arrasy Rahman, Ishan Durugkar +2
Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In standard fully cooperative multi-agent reinforcement learning, the learn…
Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents
Arrasy Rahman, Jiaxun Cui, Peter Stone
Robustly cooperating with unseen agents and human partners presents significant challenges due to the diverse cooperative conventions these partners may adopt. Existing Ad Hoc Team…
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…
A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning
Arrasy Rahman, Ignacio Carlucho, Niklas Höpner +1
Open ad hoc teamwork is the problem of training a single agent to efficiently collaborate with an unknown group of teammates whose composition may change over time. A variable team…
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…
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
Filippos Christianos, Georgios Papoudakis, Arrasy Rahman +1
Sharing parameters in multi-agent deep reinforcement learning has played an essential role in allowing algorithms to scale to a large number of agents. Parameter sharing between ag…
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning
Arrasy Rahman, Niklas Höpner, Filippos Christianos +1
Ad hoc teamwork is the challenging problem of designing an autonomous agent which can adapt quickly to collaborate with teammates without prior coordination mechanisms, including j…
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning
Georgios Papoudakis, Filippos Christianos, Arrasy Rahman +1
Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular approach which…