Batuhan Yardim
About Me
I am a PhD candidate in Computer Science at ETH Zurich, supervised by Prof. Niao He. My research focuses on the mathematical theory of reinforcement learning and multi-agent systems, with a focus on mean-field games. I am broadly interested in questions at the intersection of machine learning, optimization, and game theory.
News: I’ll be joining Jane Street’s London office as an intern in Summer 2025. I’ll also be presenting my work, “Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning,” at the Learning for Dynamics & Control (L4DC) conference at the University of Michigan from June 4–6, 2025.
Research
I work on the theory of multi-agent reinforcement learning (focusing on mean-field games and mean-field RL). This includes:
- Fundamental hardness of MF-RL
- Provably efficient algorithms for MARL with many agents
- Connections to game theory, optimization, learning theory.
Publications and Presentations
You can find a full list of my publications on my Google Scholar page.
Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning
Batuhan Yardim, Niao He
L4DC, 2025
A Variational Inequality Approach to Independent Learning in Static Mean-Field Games
Batuhan Yardim, Semih Cayci, Niao He
ACM/IMS Journal of Data Science, 2025
When is Mean-Field Reinforcement Learning Tractable and Relevant?
Batuhan Yardim, Artur Goldman, Niao He
AAMAS, 2024
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games
Batuhan Yardim, Semih Cayci, Mathhieu Geist, Niao He
ICML, 2023
Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions
Antonio Terpin, Nicolas Lanzetti, Batuhan Yardim, Florian Dorfler, Giorgia Ramponi
NeurIPS, 2022
On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation
Jiawei Huang, Batuhan Yardim, Niao He
AISTATS, 2024
Can Who-Edits-What Predict Edit Survival?
AB Yardim, V Kristof, L Maystre, M Grossglauser
KDD, 2018
Education
PhD in Computer Science
ETH Zürich, Expected graduation: 2025
Advisor: Prof. Niao He
MSc in Electrical Engineering
ETH Zürich, 2020
BSc in Mathematics and Electrical Engineering
Bilkent University, 2018