  We design a distributed algorithm for learning Nash equilibria over
time-varying communication networks in a partial-decision information scenario,
where each agent can access its own cost function and local feasible set, but
can only observe the actions of some neighbors. Our algorithm is based on
projected pseudo-gradient dynamics, augmented with consensual terms. Under
strong monotonicity and Lipschitz continuity of the game mapping, we provide a
very simple proof of linear convergence, based on a contractivity property of
the iterates. Compared to similar solutions proposed in literature, we also
allow for a time-varying communication and derive tighter bounds on the step
sizes that ensure convergence. In fact, in our numerical simulations, our
algorithm outperforms the existing gradient-based methods, when the step sizes
are set to their theoretical upper bounds. Finally, to relax the assumptions on
the network structure, we propose a different pseudo-gradient algorithm, which
is guaranteed to converge on time-varying balanced directed graphs.
