Multi-Agent Learning in Mobilized Ad-Hoc Networks.
Yu-Han Chang, Tracey Ho, Leslie Pack Kaelbling · 2004
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.