Efficiently Computing Nash Equilibria in Adversarial Team Markov Games. (arXiv:2208.02204v1 [cs.GT])

Computing Nash equilibrium policies is a central problem in multi-agent
reinforcement learning that has received extensive attention both in theory and
in practice. However, provable guarantees have been thus far either limited to
fully competitive or cooperative scenarios or impose strong assumptions that
are difficult to meet in most practical applications. In this work, we depart
from those prior results by investigating infinite-horizon \emph{adversarial
team Markov games}, a natural and well-motivated class of games in which a team
of identically-interested …

arxiv computing equilibria games markov team