Muhammad Arrasy Rahman
Postdoctoral Research Fellow at the Learning Agents Research Group.
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 | |
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| Dec 10, 2023 | |
| 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
- Minimum Coverage Sets for Training Robust Ad Hoc Teamwork AgentsProceedings 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 DiversityTransactions on Machine Learning Research, Mar 2023 - JMLRA General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy LearningJournal of Machine Learning Research, Mar 2023
- arXivMinimum Coverage Sets for Training Robust Ad Hoc Teamwork AgentsMar 2023
2022
- EUMASA survey of ad hoc teamwork researchIn European Conference on Multi-Agent Systems, Mar 2022
- arXivA General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy LearningarXiv preprint arXiv:2210.05448, Mar 2022
2021
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Towards open ad hoc teamwork using graph-based policy learningIn International Conference on Machine Learning, Mar 2021