Regret based learning algorithms have found applications in various environments including stochastic, adversarial and multi-agent ones. While optimal convergence rates were known in the stochastic and adversarial settings, the corresponding results in the multi-agent settings have started to appear only recently.
The aim of this workshop is to showcase recent trends and advances in regret-based learning algorithms in multi-agent competitive environments.
