  In this tutorial article, we give an overview of new challenges and
representative results on distributed no-regret learning in multi-agent systems
modeled as repeated unknown games. Four emerging game
characteristics---dynamicity, incomplete and imperfect feedback, bounded
rationality, and heterogeneity---that challenge canonical game models are
explored. For each of the four characteristics, we illuminate its implications
and ramifications in game modeling, notions of regret, feasible game outcomes,
and the design and analysis of distributed learning algorithms.
