Muhammad Arrasy Rahman

Postdoctoral Research Fellow at the Learning Agents Research Group.

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Gates-Dell Complex Room 3.402

2317 Speedway

The University of Texas at Austin

Austin, Texas 78712

I am a Postdoctoral Research Fellow at Professor Peter Stone’s Learning Agents Research Group (LARG) at UT Austin. Before my current position, I obtained my master’s and doctoral degrees from the University of Edinburgh, where I was a member of the Autonomous Agents Research group headed by Dr. Stefano Albrecht. I am originally from Indonesia and lived there until I obtained my undergraduate degree from Universitas Indonesia.

As real-world deployments of AI agents increase across organizations, my research aims to ensure their safe coexistence with humans and other AI agents by advancing the theoretical and practical understanding of human-AI coordination, multi-agent reinforcement learning (MARL), open-ended learning, and game theory. With a proven track record of securing substantial research funding and producing impactful research, I aim to contribute to the research of a leading research institution. I aim to lead projects that integrate multi-agent reinforcement learning and game-theoretic principles to deepen our theoretical understanding of cooperative intelligence, while developing beneficial interactive AI systems for real-world domains.

I am currently exploring open-ended learning methods to continuously generate a stream of teammate policies that highlight the trained agent’s cooperative weaknesses and lead it to improve its collaborative capabilities through interactions with them. Furthermore, my research is also interested in developing agent modeling and reasoning techniques that enable LLM-based agents to be effective partners for teammates with diverse goals and beliefs. If you are also interested in this area of research, please do not hesitate to contact me.

News

Mar 10, 2024 :newspaper_roll: Our latest work on instilling teamwork in agents via subtask curriculum has now been accepted at AAMAS 2024: Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition
Dec 10, 2023 :newspaper_roll: My latest work on generating diverse teammate policies has now been accepted at AAAI 2024: Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents
Oct 23, 2023 Together with my fellow AHT researchers, we are starting a new seminar series to disseminate current works in AHT, zero-shot coordination, agent modelling, and human-robot interaction. Check our website to find more information of upcoming meetings and presenters.
Oct 20, 2023 I’m excited to announce the start of a new partnership with Lockheed Martin Corporation. In this collaboration, we will explore methods to generate teammate policies to create more robust AHT agents.
Sep 25, 2023 We are going to organize a workshop at AAAI-24. Check out the details for our upcoming workshop here.

Selected Publications

2024

  1. Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents
    Muhammad Rahman, Jiaxun Cui, and Peter Stone
    Proceedings of the AAAI Conference on Artificial Intelligence, Mar 2024

2023

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    Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity
    Arrasy Rahman, Elliot Fosong, Ignacio Carlucho, and 1 more author
    Transactions on Machine Learning Research, Mar 2023
  2. JMLR
    A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning
    Arrasy Rahman, Ignacio Carlucho, Niklas Höpner, and 1 more author
    Journal of Machine Learning Research, Mar 2023
  3. arXiv
    Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents
    Arrasy Rahman, Jiaxun Cui, and Peter Stone
    Mar 2023

2022

  1. EUMAS
    A survey of ad hoc teamwork research
    Reuth Mirsky, Ignacio Carlucho, Arrasy Rahman, and 5 more authors
    In European Conference on Multi-Agent Systems, Mar 2022
  2. arXiv
    A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning
    Arrasy Rahman, Ignacio Carlucho, Niklas Höpner, and 1 more author
    arXiv preprint arXiv:2210.05448, Mar 2022

2021

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    Towards open ad hoc teamwork using graph-based policy learning
    Muhammad A Rahman, Niklas Hopner, Filippos Christianos, and 1 more author
    In International Conference on Machine Learning, Mar 2021