We consider a variant of the classic multi-armed bandit problem (MAB), which we call FEEDBACK MAB, where the reward obtained by playing each of n independent arms varies according...
— The paper is concerned with a novel adaptive game server protocol optimization to combat network latencies in the case of heterogeneous network environment. In this way, game p...
We consider two-player games played for an infinite number of rounds, with -regular winning conditions. The games may be concurrent, in that the players choose their moves simulta...
Classically, an approach to the multiagent policy learning supposed that the agents, via interactions and/or by using preliminary knowledge about the reward functions of all playe...
Multiagent learning literature has investigated iterated twoplayer games to develop mechanisms that allow agents to learn to converge on Nash Equilibrium strategy profiles. Such ...