Mobilized ad-hoc networks: a reinforcement learning approach

With the cost of wireless networking and computational power rapidly dropping, mobile ad-hoc networks will soon become an important part of our society's computing structures. While there is a great deal of research from the networking community regarding the routing of information over such networks, most of these techniques lack automatic adaptivity. The size and complexity of these networks demand that we apply the principles of autonomic computing to this problem. Reinforcement learning methods can be used to control both packet routing decisions and node mobility, dramatically improving the connectivity of the network. We present two applications of reinforcement learning methods to the mobilized ad-hoc networking domain and demonstrate some promising empirical results under a variety of different scenarios in which the mobile nodes in our ad-hoc network are embedded with these adaptive routing policies and learned movement policies.