Solving Delayed Coordination Problems in MAS

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. This restriction is not suitable for realistic systems, where rewards can be delayed and often conflicts between agents become apparent only several time-steps after an action has been taken. In this paper, we contribute a reinforcement learning algorithm that learns where a strategic interaction among agents is needed, several time-steps before the conflict is reflected by the (immediate) reward signal