Modelling the Dynamics of Regret Minimization in Large Agent Populations: a Master Equation Approach
Zhen Wang, Chunjiang Mu, Shuyue Hu, Chen Chu, Xuelong Li
Abstract
Understanding the learning dynamics in multiagent systems is an important and challenging task. Past research on multi-agent learning mostly focuses on two-agent settings. In this paper, we consider the scenario in which a population of infinitely many agents apply regret minimization in repeated symmetric games. We propose a new formal model based on the master equation approach in statistical physics to describe the evolutionary dynamics in the agent population. Our model takes the form of a partial differential equation, which describes how the probability distribution of regret evolves over time. Through experiments, we show that our theoretical results are consistent with the agent-based simulation results.
BibTeX
@inproceedings{ijcai2022p76,
title = {Modelling the Dynamics of Regret Minimization in Large Agent Populations: a Master Equation Approach},
author = {Wang, Zhen and Mu, Chunjiang and Hu, Shuyue and Chu, Chen and Li, Xuelong},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {534--540},
year = {2022},
month = {7},
note = {Main Track},
doi = {10.24963/ijcai.2022/76},
url = {https://doi.org/10.24963/ijcai.2022/76},
}