Learning Stationary Nash Equilibrium Policies in $n$-Player Stochastic Games with Independent Chains via Dual Mirror Descent. (arXiv:2201.12224v1 [cs.LG])

We consider a subclass of $n$-player stochastic games, in which players have
their own internal state/action spaces while they are coupled through their
payoff functions. It is assumed that players' internal chains are driven by
independent transition probabilities. Moreover, players can only receive
realizations of their payoffs but not the actual functions, nor can they
observe each others' states/actions. Under some assumptions on the structure of
the payoff functions, we develop efficient learning algorithms based on Dual
Averaging and Dual …

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