AI RESEARCH

Choose Your Battles: Distributed Learning Over Multiple Tug of War Games

arXiv CS.LG

ArXi:2509.20147v2 Announce Type: replace-cross Consider $N$ players and $K$ games taking place simultaneously. Each of these games is modeled as a Tug-of-War (ToW) game where increasing the action of one player decreases the reward for all other players. Each player participates in only one game at any given time. At each time step, a player decides the game in which they wish to participate in and the action they take in that game. Their reward depends on the actions of all players that are in the same game. This system of $K$ games is termed a 'Meta Tug-of-War' (Meta-ToW) game.