Anas Barakat

Anas Barakat

Research Fellow

Singapore University of Technology and Design

I am currently a research fellow at Singapore University of Technology and Design working with Georgios Piliouras and Antonios Varvitsiotis.

My research develops theory and algorithms for learning in strategic, structured, and dynamic environments. I study how autonomous agents learn to act in multi-agent settings where structure, feedback loops and non-standard or non-stationary objectives shape long-term behavior. My work integrates tools from reinforcement learning, game theory, online learning, stochastic optimization, and dynamical systems to build principled and robust multi-agent learning systems.

See below for a more detailed overview of my research.

Previously, I was a postdoctoral fellow at ETH Zurich working with Niao He in the department of computer science. I obtained my PhD in applied mathematics and computer science from Institut Polytechnique de Paris at Télécom Paris under the supervision of Pascal Bianchi and Walid Hachem. I received my engineering Master's degree in applied mathematics and computer science from Télécom Paris and a Master's degree in data science from Université Paris Saclay.

Here is my CV for more information.

News

  • Oct. 2025: Designing and teaching a new course with John Lazarsfeld and Iosif Sakos. Check out our website: Online Learning and Learning in Games.
  • Sep. 2025: `On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning’ accepted to NeurIPS 2025.

Interests

  • Multi-Agent Learning
  • Reinforcement Learning
  • Stochastic Optimization

Education

  • PhD in Applied Mathematics and CS, 2021

    Institut Polytechnique de Paris (Télécom Paris)

  • MSc in Data Science, 2018

    Université Paris Saclay

  • MSc in Applied Mathematics and CS, 2018

    Télécom Paris

Research

Multi-Agent Learning in Strategic and Dynamic Environments

  • Learning in Markov games
  • Multi-Agent Reinforcement Learning
  • Online learning in games
Anas Barakat, John Lazarsfeld, Georgios Piliouras, Antonios Varvitsiotis (2025). Online Multi-Agent Control with Adversarial Disturbances. Under review.

Arxiv

Anas Barakat, Wayne Lin, John Lazarsfeld, Antonios Varvitsiotis (2025). Optimistic Online Learning in Symmetric Cone Games. Under review.

Arxiv

Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He (2025). Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last Iterate Convergence. SIAM Journal on Control and Optimization 63 (5), 3244-3271.

Arxiv

Pragnya Alatur, Anas Barakat*, Niao He (2024). Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players. IEEE CDC 2024. *corresponding author.

Slides Proceedings Arxiv

Philip Jordan, Anas Barakat, Niao He (2023). Independent Learning in Constrained Markov Potential Games. AISTATS 2024.

Poster Slides Proceedings Arxiv

Jiduan Wu, Anas Barakat, Ilyas Fatkhullin, Niao He (2023). Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity. IEEE CDC 2023.

Proceedings Arxiv

Reinforcement Learning

  • RL with general utilities, convex RL
  • Policy gradient algorithms
  • Behaviorally aligned RL inspired from behavioral economics
Olivier Lepel, Anas Barakat (2025). A Prospect-Theoretic Policy Gradient Framework for Behaviorally Nuanced Reinforcement Learning. Under review.

Slides Arxiv

Anas Barakat, Souradip Chakraborty, Peihong Yu, Pratap Tokekar, Amrit Singh Bedi (2025). On the Global Optimality of Policy Gradient Methods in General Utility Reinforcement Learning. NeurIPS 2025.

Arxiv

Kimon Protopapas, Anas Barakat (2024). Policy Mirror Descent with Lookahead. NeurIPS 2024.

Proceedings Arxiv

Anas Barakat, Ilyas Fatkhullin, Niao He (2023). Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space. ICML 2023.

Poster Slides Proceedings Arxiv

Ilyas Fatkhullin, Anas Barakat, Anastasia Kireeva, Niao He (2023). Stochastic Policy Gradient Methods: Improved Sample Complexity for Fisher-non-degenerate Policies. ICML 2023.

Poster Proceedings Arxiv

Anas Barakat, Pascal Bianchi, Julien Lehmann (2022). Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation. AISTATS 2022.

Poster Proceedings Arxiv

Stochastic Optimization

  • Stochastic approximation
  • Dynamical systems
  • Non-convex stochastic optimization
  • Adaptive gradient methods
Anas Barakat, Pascal Bianchi, Walid Hachem, Sholom Schechtman (2021). Stochastic optimization with momentum: convergence, fluctuations, and traps avoidance. Electronic Journal of Statistics 15 (2), 3892-3947.

DOI Arxiv Journal

Anas Barakat, Pascal Bianchi (2020). Convergence and Dynamical Behavior of the ADAM Algorithm for Non-Convex Stochastic Optimization. SIAM Journal on Optimization, 31 (1), 244-274.

Poster Slides Video DOI Arxiv Journal

Anas Barakat, Pascal Bianchi (2019). Convergence Rates of a Momentum Algorithm with Bounded Adaptive Step Size for Non-Convex Optimization. ACML 2020.

Poster Slides Proceedings Arxiv

Talks

Invited talk - ICCOPT 2025
Jul 24, 2025 3:00 PM University of Southern California, Los Angeles, USA
Invited talk - Learning Theory and Applications Workshop, NTU
Apr 29, 2025 4:00 PM College of Computing and Data Science, NTU, Singapore
Invited talk - Finance and RL Talks
Apr 21, 2025 10:00 PM Online
Invited talk - 4th Symposium on Machine Learning and Dynamical Systems
Jul 8, 2024 2:45 PM Fields Institute, Toronto, Canada
Talk - SUTD
May 3, 2024 10:00 AM Online
See all talks

Reviewing

  • Conferences: NeurIPS, ICML, ICLR, COLT, AISTATS, AAAI, IEEE CDC.

  • Journals: Journal of Machine Learning Research (JMLR), Transactions on Machine Learning Research (TMLR), Mathematical Programming, SIAM Journal on Optimization (SIOPT), Journal of Optimization Theory and Applications (JOTA), Systems & Control Letters, Mathematics of Control, Signals, and Systems (MCSS), Stochastic Systems, IEEE Transactions on Automatic Control, IEEE Control Systems Letters.

Teaching

SUTD (2024-2025):

ETH Zurich (2022-2024):

Télécom Paris (2018-2021): Teaching Assistant

Optimization for Machine Learning (graduate level), Statistics (graduate level), Probabilities (undergraduate level).

Contact