Games in Machine Learning
MSc course, summer 2025/2026
TU Wien

Multi-Player Learning in Modern Machine Learning

Many contemporary machine learning systems involve multiple interacting agents, each optimizing distinct—often conflicting—objectives. These scenarios arise in settings such as Stackelberg games, generative adversarial networks (GANs), multi-agent reinforcement learning (MARL), collaborative robotics, and competitive drone systems. Even standard single-model training can exhibit game-theoretic structures, including adversarial robustness, learning under distributional shift, and causal inference.

This course introduces students to the theory and practice of learning in multi-player systems, with a strong focus on gradient-based methods and convergence to equilibrium solutions. We will explore:

• Foundations: Core principles from convex optimization and zero-sum min-max games.
• Unifying Framework: Variational Inequalities (VIs) as a general analytical tool for multi-player learning.
• Applications: Practical challenges in multi-agent RL, GAN training dynamics, and beyond.

Key Takeaways

• Understand iterative learning dynamics in single- and multi-agent environments.
• Identify and address challenges in min-max and non-convex optimization.
• Apply a unified VI-based framework to analyze n-player learning systems.
• Gain practical skills for designing ML systems in multi-agent settings.

By the end of the course, students will be well-equipped to design, analyze, and implement learning algorithms for complex, interactive ML systems—bridging foundational theory with cutting-edge applications.

Lecturer:

• Tatjana Chavdarova is Assistant professor at TU Wien, with a research focus on the intersection of game theory and machine learning.

Course Materials

• Lecture 1: Intro, course organization, game-theoretic solutions

  • Slides - Part 1 • Slides - Part 2 • Exercises • Solutions
  Covers:
  • Motivation to Study Games in ML
  • What Makes a Game? (an ML perspective)
  • Games & Intelligence: Historical Remarks
  • Game Theoretic Solution Concepts
  • Optimization Intro
  

Resources & Main References

Contact

For typos, remarks, or you'd like to use the latex code of the slides feel free to reach out at: tatjana.chavdarova[at]tuwien[dot]ac.at.