Learning Risk-Averse Equilibria in Multi-Agent Systems. (arXiv:2205.15434v1 [cs.LG])

In multi-agent systems, intelligent agents are tasked with making decisions
that have optimal outcomes when the actions of the other agents are as
expected, whilst also being prepared for unexpected behaviour. In this work, we
introduce a new risk-averse solution concept that allows the learner to
accommodate unexpected actions by finding the minimum variance strategy given
any level of expected return. We prove the existence of such a risk-averse
equilibrium, and propose one fictitious-play type learning algorithm for
smaller games …

arxiv equilibria learning risk systems