Efficiently Computing Nash Equilibria in Adversarial Team Markov Games

no code implementations • 3 Aug 2022 • Fivos Kalogiannis, Ioannis Anagnostides, Ioannis Panageas, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Vaggos Chatziafratis, Stelios Stavroulakis

In this work, we depart from those prior results by investigating infinite-horizon \emph{adversarial team Markov games}, a natural and well-motivated class of games in which a team of identically-interested players -- in the absence of any explicit coordination or communication -- is competing against an adversarial player.

Multi-agent Reinforcement Learning

Reinforcement Learning for Location-Aware Scheduling

no code implementations • 7 Mar 2022 • Stelios Stavroulakis, Biswa Sengupta

Recent techniques in dynamical scheduling and resource management have found applications in warehouse environments due to their ability to organize and prioritize tasks in a higher temporal resolution.

Management reinforcement-learning +2