Year: 2022
Type: preprint
Abstract: We develop a flexible stochastic approximation framework for analyzing the long-run behavior of learning in games (both continuous and finite). The proposed analysis template incorporates a wide array of popular learning algorithms, including gradient-based methods, the exponential/multiplicative weights algorithm for learning in finite games, optimistic and bandit variants of the above, etc. In addition to providing an integrated view of these algorithms, our framework further allows us to obtain several new convergence results, both asymptotic and in finite time, in both continuous and finite games. Specifically, we provide a range of criteria for identifying classes of Nash equilibria and sets of action profiles that are attracting with high probability, and we also introduce the notion of coherence, a game-theoretic property that includes strict and sharp equilibria, and which leads to convergence in finite time. Importantly, our analysis applies to both oracle-based and bandit, payoff-based methods - that is, when players only observe their realized payoffs.
Source: arXiv (Cornell University)
Authors: Mertikopoulos, Panayotis, Hsieh, Ya-Ping, Cevher, Volkan
Institutions: Institut polytechnique de Grenoble, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Laboratoire d'Informatique de Grenoble, Université Grenoble Alpes, Institute for Machine Learning, École Polytechnique Fédérale de Lausanne
Language: English
Topic: Game Theory and Applications
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4281707678