I am a Research Assistant at Meta AI (FAIR) and third-year PhD student at University College London (UCL). At UCL, I am supervised by Tim Rocktäschel and am a member of the UCL Deciding, Acting, and Reasoning with Knowledge (DARK) Lab. I am 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 previously held research and development engineering positions at Reddit, Mentor, Toptal and USC Information Sciences Institute.
My research interests lie in the areas of deep reinforcement learning, multi-agent learning, and open-endedness.
Contact: mikayel [at] samvelyan [dot] com

News
- [Mar-May 2023] Invited talks at UC Berkeley MARL Seminar, InstaDeep, and University of Maryland.
- [Feb 2023] MAESTRO has been accepted to ICLR 2023.
- [Dec 2022] We released SMACv2, an improved version of the StarCraft Multi-Agent Challenge.
Libraries
Featured Media
Selected Publications
See Google Scholar for more publications.
Journal Papers

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}, }
Conference Papers

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
International Conference on Learning Representations (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} }

GriddlyJS: A Web IDE for Reinforcement
Learning
C Bamford, M Jiang,
M Samvelyan,
T Rocktäschel
Conference on Neural Information Processing Systems
(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} }

Evolving
Curricula with Regret-Based Environment Design
J Parker-Holder*, M Jiang*,
M Dennis,
M Samvelyan,
J Foerster,
E Grefenstette,
T Rocktäschel
International Conference on Machine Learning (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} }

Hierarchical
Kickstarting for Skill Transfer in
Reinforcement Learning
M Matthews,
M Samvelyan,
J Parker-Holder, E Grefenstette,
T Rocktäschel
Conference on Lifelong Learning Agents (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}, }

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
Conference on Neural Information Processing Systems
(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} }

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
International Conference on Machine Learning (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:
Multi-Agent Variational Exploration
A Mahajan,
T Rashid, M Samvelyan,
S Whiteson
Conference on Neural Information Processing Systems
(NeurIPS), 2019

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
International Conference on Autonomous Agents and Multiagent
Systems (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:
Monotonic Value Function Factorisation for Deep
Multi-Agent Reinforcement Learning
T Rashid*, M Samvelyan*,
C Schroeder de Witt,
G Farquhar, J Foerster,
S Whiteson
International Conference on Machine Learing (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}, }
Preprints

SMACv2:
An Improved Benchmark for Cooperative Multi-Agent
Reinforcement Learning
B Ellis, S Moalla,
M Samvelyan,
M Sun, A Mahajan,
J Foerster,
S Whiteson
arXiv, 2022
@misc{ellis2022smacv2, url = {https://arxiv.org/abs/2212.07489}, author = {Ellis, Benjamin and Moalla, Skander and Samvelyan, Mikayel and Sun, Mingfei and Mahajan, Anuj and Foerster, Jakob N. and Whiteson, Shimon}, title = {SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning}, publisher = {arXiv}, year = {2022}, }

Generalization in Cooperative Multi-Agent
Systems
A Mahajan,
M Samvelyan,
T Gupta,
B Ellis,
M Sun, T Rocktäschel,
S Whiteson
arXiv, 2022
Teaching
- Spring 2023 - COMP0087 Statistical Natural Language Processing (TA)
- Spring 2022 - COMP0089 Reinforcement Learning (TA)
- Spring 2022 - COMP0087 Statistical Natural Language Processing (TA)
- Spring 2021 - COMP0089 Reinforcement Learning (TA)
- Spring 2021 - COMP0087 Statistical Natural Language Processing (TA)
- Spring 2020 - Data Structures (TA)
- Fall 2019 - Machine Learning (Lecturer)
- Fall 2018 - Machine Learning (Lecturer)
- Fall 2018 - Operating Systems (TA)
- Fall 2018 - Artificial Intelligence (Guest Lecturer and TA)