The interaction of multiple autonomous agents gives rise to highly dynamic and
nondeterministic environments, contributing to the complexity in applications such as …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

In this paper we investigate the Follow the Regularized Leader dynamics in sequential
imperfect information games (IIG). We generalize existing results of Poincar {é} recurrence …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Exploration-exploitation is a powerful and practical tool in multi-agent learning (MAL);
however, its effects are far from understood. To make progress in this direction, we study a …

The interplay between exploration and exploitation in competitive multi-agent learning is still
far from being well understood. Motivated by this, we study smooth Q-learning, a prototypical …

We consider the dynamics of Q learning in two-player two-action games with a Boltzmann
exploration mechanism. For any nonzero exploration rate the dynamics is dissipative, which …

A dynamical systems perspective on multi-agent learning, based on the link between
evolutionary game theory and reinforcement learning, provides an improved, qualitative …

Arguably one of the thorniest problems in game theory is that of equilibrium selection.
Specifically, in the presence of multiple equilibria do self-interested learning dynamics …