Future sparse interactions: a MARL approach

Recent research has demonstrated that considering local interactions among agents in specific parts of the state space, is a successful way of simplifying the multi-agent learning process. By taking into account other agents only when a conflict is possible, an agent can significantly reduce the state-action space in which it learns. Current approaches, however, consider only the immediate rewards for detecting conflicts. In this paper, we contribute a reinforcement learning algorithm that learns when a strategic interaction among agents is needed, several time-steps before the conflict is reflected by the (immediate) reward signal.