  We consider decentralized learning for zero-sum games, where players only see
their payoff information and are agnostic to actions and payoffs of the
opponent. Previous works demonstrated convergence to a Nash equilibrium in this
setting using double time-scale algorithms under strong reachability
assumptions. We address the open problem of achieving an approximate Nash
equilibrium efficiently with an uncoupled and single time-scale algorithm under
weaker conditions. Our contribution is a rational and convergent algorithm,
utilizing Tsallis-entropy regularization in a value-iteration-based approach.
The algorithm learns an approximate Nash equilibrium in polynomial time,
requiring only the existence of a policy pair that induces an irreducible and
aperiodic Markov chain, thus considerably weakening past assumptions. Our
analysis leverages negative drift inequalities and introduces novel properties
of Tsallis entropy that are of independent interest.
