I'm a final year PhD student at Meta AI (FAIR) and University College London (UCL). At UCL, I am supervised by Tim Rocktäschel at UCL DARK Lab. I'm also part of the ELLIS PhD & Postdoc Program.

I hold an MSc in Computer Science degree from the University of Oxford where I worked in the Whiteson Research Lab advised by Shimon Whiteson. Prior to that, I obtained Master’s and Bachelor’s degrees from Yerevan State University in Informatics and Applied Mathematics. I have held research and development positions at Reddit, Mentor, Toptal and USC Information Sciences Institute.

My research focuses on reinforcement learning, multi-agent learning, and open-endedness. My early works include widely-used tools for multi-agent RL, such as the QMIX method and SMAC benchmark. Much of my follow up work focuses on using open-ended learning to train generally capable RL agents and diagnose their robustness. Recently, I used these techniques to enhance the safety of LLMs with approaches like Rainbow Teaming, which identifies vulnerabilities and generates synthetic data to improve LLM robustness, and also contributed to Meta Llama 3.

My long-term goal is to develop methods that give AI agents endless learning opportunities, enabling them to perform an ever-expanding range of tasks and become increasingly robust.

Contact: mikayel [at] samvelyan [dot] com

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Highlighted Papers

rainbow

Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
M Samvelyan*, S Raparthy*, A Lupu*, E Hambro, A Markosyan, M Bhatt, Y Mao, M Jiang, J Parker-Holder, J Foerster, T Rocktäschel, R Raileanu
arXiv, 2024

@misc{samvelyan2024rainbow,
   title={Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts}, 
   author={Mikayel Samvelyan and Sharath Chandra Raparthy and Andrei Lupu and Eric Hambro and Aram H. Markosyan and Manish Bhatt and Yuning Mao and Minqi Jiang and Jack Parker-Holder and Jakob Foerster and Tim Rocktäschel and Roberta Raileanu},
   year={2024},
   eprint={2402.16822},
   archivePrefix={arXiv},
   primaryClass={cs.CL}
}
madrid

Multi-Agent Diagnostics for Robustness via Illuminated Diversity
M Samvelyan*, D Paglieri*, M Jiang, J Parker-Holder, T Rocktäschel
AAMAS, 2024 (Oral)

@misc{samvelyan2024multiagent,
   title={Multi-Agent Diagnostics for Robustness via Illuminated Diversity}, 
   author={Mikayel Samvelyan and Davide Paglieri and Minqi Jiang and Jack Parker-Holder and Tim Rocktäschel},
   year={2024},
   eprint={2401.13460},
   archivePrefix={arXiv},
   primaryClass={cs.LG}
}
MAESTRO

MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning
M Samvelyan, A Khan, M Dennis, M Jiang, J Parker-Holder, J Foerster, R Raileanu, T Rocktäschel
ICLR, 2023

@inproceedings{samvelyan2023maestro,
   title={{MAESTRO}: Open-Ended Environment Design for Multi-Agent Reinforcement Learning},
   author={Mikayel Samvelyan and Akbir Khan and Michael D Dennis and Minqi Jiang and Jack Parker-Holder and Jakob Nicolaus Foerster and Roberta Raileanu and Tim Rockt{\"a}schel},
   booktitle={International Conference on Learning Representations},
   year={2023},
   url={https://openreview.net/forum?id=sKWlRDzPfd7}
}
MiniHack

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
M Samvelyan, R Kirk, V Kurin, J Parker-Holder, M Jiang, E Hambro, F Petroni, H Küttler, E Grefenstette, T Rocktäschel
NeurIPS, 2021

@inproceedings{samvelyan2021minihack,
   title={MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research},
   author={Mikayel Samvelyan and Robert Kirk and Vitaly Kurin and Jack Parker-Holder and Minqi Jiang and Eric Hambro and Fabio Petroni and Heinrich Kuttler and Edward Grefenstette and Tim Rockt{\"a}schel},
   booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},
   year={2021},
   url={https://openreview.net/forum?id=skFwlyefkWJ}
}
qmix_journal

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
T Rashid*, M Samvelyan*, C Schroeder de Witt, G Farquhar, J Foerster, S Whiteson
Journal of Machine Learning Research (JMLR), 2020

@article{rashid20monotonic,
   author  = {Tabish Rashid and Mikayel Samvelyan and Christian Schroeder de Witt and Gregory Farquhar and Jakob Foerster and Shimon Whiteson},
   title   = {Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning},
   journal = {Journal of Machine Learning Research},
   year    = {2020},
   volume  = {21},
   number  = {178},
   pages   = {1--51},
}
SMAC

The StarCraft Multi-Agent Challenge
M Samvelyan*, T Rashid*, C Schroeder de Witt, G Farquhar, N Nardelli, T Rudner, C Hung, P Torr, J Foerster, S Whiteson
AAMAS, 2019

@inproceedings{samvelyan2019smac,
   title = {{The} {StarCraft} {Multi}-{Agent} {Challenge}},
   author = {Samvelyan, Mikayel and Rashid, Tabish and Schroeder de Witt, Christian and Farquhar, Gregory and Nardelli, Nantas and Rudner, Tim G. J. and Hung, Chia-Man and Torr, Philip H. S. and Foerster, Jakob and Whiteson, Shimon},
   booktitle = {Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems},
   pages = {2186--2188},
   year = {2019},
}
QMIX

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
T Rashid*, M Samvelyan*, C Schroeder de Witt, G Farquhar, J Foerster, S Whiteson
ICML, 2018

@inproceedings{rashid18qmix,
   title = {{QMIX}: {Monotonic} {Value} {Function} {Factorisation} {for} {Deep} {Multi}-{Agent} {Reinforcement} {Learning}},
   author = {Rashid, Tabish and Samvelyan, Mikayel and Schroeder, Christian and Farquhar, Gregory and Foerster, Jakob and Whiteson, Shimon},
   booktitle = {Proceedings of the 35th International Conference on Machine Learning},
   publisher = {PMLR},
   volume = {80},
   pages = {4295--4304},
   year = {2018},
}

Other Selected Papers

See Google Scholar for more publications.

craftax

Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
M Matthews, M Beukmans, B Ellis, M Samvelyan, M Jackson, S Coward, J Foerster
ICML, 2024 (Spotlight)

@article{matthews2024craftax,
   title={Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning},
   author={Michael Matthews and Michael Beukman and Benjamin Ellis and Mikayel Samvelyan and Matthew Jackson and Samuel Coward and Jakob Foerster},
   journal={arXiv preprint},
   year={2024},
}
jaxmarl

JaxMARL: Multi-Agent RL Environments in JAX
A Rutherford, B Ellis, M Gallici, J Cook, A Lupu, G Ingvarsson, T Willi, A Khan, C Schroeder de Witt, A Souly, S Bandyopadhyay, M Samvelyan, M Jiang, R Lange, S Whiteson, B Lacerda, N Hawes, T Rocktäschel, C Lu, J Foerster
AAMAS, 2024

@misc{rutherford2023jaxmarl,
   title={JaxMARL: Multi-Agent RL Environments in JAX}, 
   author={Alexander Rutherford and Benjamin Ellis and Matteo Gallici and Jonathan Cook and Andrei Lupu and Gardar Ingvarsson and Timon Willi and Akbir Khan and Christian Schroeder de Witt and Alexandra Souly and Saptarashmi Bandyopadhyay and Mikayel Samvelyan and Minqi Jiang and Robert Tjarko Lange and Shimon Whiteson and Bruno Lacerda and Nick Hawes and Tim Rocktaschel and Chris Lu and Jakob Nicolaus Foerster},
   year={2023},
   eprint={2311.10090},
   archivePrefix={arXiv},
   primaryClass={cs.LG}
}
SMACv2

SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning
B Ellis, J Cook, S Moalla, M Samvelyan, M Sun, A Mahajan, J Foerster, S Whiteson
NeurIPS, 2023

 @inproceedings{ellis2023smacv2,
    title={{SMAC}v2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning},
    author={Benjamin Ellis and Jonathan Cook and Skander Moalla and Mikayel Samvelyan and Mingfei Sun and Anuj Mahajan and Jakob Nicolaus Foerster and Shimon Whiteson},
    booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
    year={2023},
    url={https://openreview.net/forum?id=5OjLGiJW3u}
}
mixme

Mix-ME: Quality-Diversity for Multi-Agent Learning
G Ingvarsson, M Samvelyan, M Flageat, B Lim, A Cully, T Rocktäschel
ALOE Workshop @ NeurIPS, 2023

@inproceedings{ingvarsson2023mixme,
   title={Mix-{ME}: Quality-Diversity for Multi-Agent Learning},
   author={Gar{\dh}ar Ingvarsson and Mikayel Samvelyan and Manon Flageat and Bryan Lim and Antoine Cully and Tim Rockt{\"a}schel},
   booktitle={Second Agent Learning in Open-Endedness Workshop},
   year={2023},
   url={https://openreview.net/forum?id=acD8BxMjwV}
}
GriddlyJS

GriddlyJS: A Web IDE for Reinforcement Learning
C Bamford, M Jiang, M Samvelyan, T Rocktäschel
NeurIPS, 2022

@inproceedings{bamford2022griddlyjs,
   title={Griddly{JS}: A Web {IDE} for Reinforcement Learning},
   author={Christopher Bamford and Minqi Jiang and Mikayel Samvelyan and Tim Rockt{\"a}schel},
   booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
   year={2022},
   url={https://openreview.net/forum?id=YmacJv0i_UR}
}
Accel

Evolving Curricula with Regret-Based Environment Design
J Parker-Holder*, M Jiang*, M Dennis, M Samvelyan, J Foerster, E Grefenstette, T Rocktäschel
ICML, 2022

@article{parkerholder2022evolving,
   title={Evolving Curricula with Regret-Based Environment Design},
   author={Parker-Holder, Jack and Jiang, Minqi and Dennis, Michael D and Samvelyan, Mikayel and Foerster, Jakob Nicolaus and Grefenstette, Edward and Rockt{\"a}schel, Tim},
   journal={arXiv preprint arXiv:2203.01302},
   year={2022}
}
SkillHack

Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning
M Matthews, M Samvelyan, J Parker-Holder, E Grefenstette, T Rocktäschel
CoLLAs, 2022

@misc{matthews2022hierarchical,
   url = {https://arxiv.org/abs/2207.11584},
   author = {Matthews, Michael and Samvelyan, Mikayel and Parker-Holder, Jack and Grefenstette, Edward and Rocktäschel, Tim},
   keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
   title = {Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning},
   publisher = {arXiv},
   year = {2022},
}
genmas

Generalization in Cooperative Multi-Agent Systems
A Mahajan, M Samvelyan, T Gupta, B Ellis, M Sun, T Rocktäschel, S Whiteson
arXiv, 2022

@article{mahajan2022generalization, 
   title={Generalization in Cooperative Multi-Agent Systems}, 
   author={Mahajan, Anuj and Samvelyan, Mikayel and Gupta, Tarun and Ellis, Benjamin and Sun, Mingfei and Rockt{\"a}schel, Tim and Whiteson, Shimon}, 
   journal={arXiv preprint arXiv:2202.00104}, 
   year={2022},
}
Tesseract

Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
A Mahajan, M Samvelyan, L Mao, V Makoviychuk, A Garg, J Kossaifi, S Whiteson, Y Zhu, A Anandkumar
ICML, 2021

@inproceedings{mahajan2021tesseract,
   title = {Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning},
   author = {Mahajan, Anuj and Samvelyan, Mikayel and Mao, Lei and Makoviychuk, Viktor and Garg, Animesh and Kossaifi, Jean and Whiteson, Shimon and Zhu, Yuke and Anandkumar, Animashree},
   booktitle = {Proceedings of the 38th International Conference on Machine Learning},
   publisher = {PMLR},
   volume = {139},
   pages = {7301--7312},
   year = {2021},
}
MAVEN

MAVEN: Multi-Agent Variational Exploration
A Mahajan, T Rashid, M Samvelyan, S Whiteson
NeurIPS, 2019

@incollection{mahajan2019maven,
   title = {{MAVEN}: {Multi}-{Agent} {Variational} {Exploration}},
   author = {Mahajan, Anuj and Rashid, Tabish and Samvelyan, Mikayel and Whiteson, Shimon},
   booktitle = {Advances in Neural Information Processing Systems 32},
   pages = {7611--7622},
   year = {2019},
}

Teaching

  • Spring 2020 - Data Structures (TA)
  • Fall 2019 - Machine Learning (Lecturer)
  • Fall 2018 - Machine Learning (Lecturer)
  • Fall 2018 - Operating Systems (TA)