  Multi-agent learning is intrinsically harder, more unstable and unpredictable
than single agent optimization. For this reason, numerous specialized
heuristics and techniques have been designed towards the goal of achieving
convergence to equilibria in self-play. One such celebrated approach is the use
of dynamically adaptive learning rates. Although such techniques are known to
allow for improved convergence guarantees in small games, it has been much
harder to analyze them in more relevant settings with large populations of
agents. These settings are particularly hard as recent work has established
that learning with fixed rates will become chaotic given large enough
populations.In this work, we show that chaos persists in large population
congestion games despite using adaptive learning rates even for the ubiquitous
Multiplicative Weight Updates algorithm, even in the presence of only two
strategies. At a technical level, due to the non-autonomous nature of the
system, our approach goes beyond conventional period-three techniques Li-Yorke
by studying fundamental properties of the dynamics including invariant sets,
volume expansion and turbulent sets. We complement our theoretical insights
with experiments showcasing that slight variations to system parameters lead to
a wide variety of unpredictable behaviors.
