Regret, equilibrium, and learning in games: A guided tour
This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond. Our presentation is structured around two complementary viewpoints: We first consider a single agent -- the learner -- engaged in a sequential decision process in an unknown, non-stationary, and possibly adversarial environment. We then examine what happens when the environment is shaped by the decisions of several interacting agents, not necessarily aware of each other's actions or goals, and all seeking to improve their individual rewards. In this general context, ...
| Authors | Panayotis Mertikopoulos |
|---|---|
| Primary category | cs.GT |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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