Multi-agent learning in mobilized ad-hoc networks

In large, distributed systems such as mobilized ad-hoc networks, centralized learning of routing or movement policies may be impractical. We need to employ multi-agent learning algorithms that can learn independently, without the need for extensive coordination. Using only a simple coordination signals such as a global reward value, we show that reinforcement learning methods can be used to control both packet routing decisions and node mobility.